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Emerging Markets

Banking

in an

UPSIDE DOWN WORLD Challenging Perceptions in Asset Allocation and Investment JE RO ME BO OT H

Understanding and Addressin g the Failures in Risk Managem ent, Governance an d Regulation

A D R IA N D O C H ER FR A N C K V IO T Y RT

ANDREW SMITHERS Foreword by Martin Wolf, r, Financial Times Chief Economics Commentato

THE ROAD TO HOW AND WHY GE ECONOMIC POLICY MUST CHAN


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INTRODUCTION

The Wiley e-book Sampler includes selected material from Wiley’s leading titles on Banking & Finance, including the book’s full Table of Contents as well as a full sample chapter. The list of titles included are: Emerging Markets in an Upside Down World: Challenging Perceptions in Asset Allocation and Investment 9781118879672, March 2014

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Global Leaders in Islamic Finance: Industry Milestones and Reflections 9781118465240, November 2013

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Better Banking: Understanding and Addressing the Failures in Risk Management, Governance and Regulation 9781118651308, December 2013

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Rebuilding Trust in Banks: The Role of Leadership and Governance 9781118550380, November 2013

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Correlation Risk Modeling and Management: An Applied Guide including the Basel III Correlation Framework - With Interactive Models in Excel/VBA 9781118796900, November 2013

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The Bank Credit Analysis Handbook, Second Edition: A Guide for Analysts, Bankers, and Investors 9780470821572, April 2013

154

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Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading, 2nd Edition 9781118362419, March 2013

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The Road to Recovery: How and Why Economic Policy Must Change 9781118515662, September 2013 }} Table of Contents }} Chapter 1

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Emerging Markets in an

UPSIDE DOWN WORLD Challenging Perceptions in Asset Allocation and Investment JEROME BOOTH


Contents Foreword by Nigel Lawson

xi

Acknowledgements

xiii

Introduction I.1 Upside down: perception vs reality I.2 The structure of the book 1

Globalisation and the Current Global Economy 1.1 What is globalisation? 1.2 Economic history and globalisation 1.2.1 The desire to control and its impact on trade 1.2.2 The influence of money 1.2.3 Trade and commodification 1.2.4 Nationalism 1.3 Recent globalisation 1.3.1 Bretton Woods 1.3.2 Ideological shifts 1.3.3 Participating in globalisation: living with volatility

2 Defining Emerging Markets 2.1 The great global rebalancing 2.1.1 Financing sovereigns 2.1.2 Catching up 2.1.3 The poorest can also emerge: aid and debt 2.1.4 From debt to transparency and legitimacy 2.2 Investing in emerging markets 2.3 Emerging market debt in the 20th century 2.3.1 Types of external sovereign debt 2.3.2 From Mexican crisis to Brady bonds 2.3.3 Market discipline 2.3.4 Eastern Europe 2.3.5 Mexico in crisis again

1 2 4 9 9 12 13 15 16 17 18 19 21 23 25 25 26 27 29 30 31 32 33 36 39 40 41

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2.3.6 2.3.7

2.4 2.5

The Asian and Russian crises Emerging markets grow up (a) The first change: country and contagion risks fall (b) The second change: the investor base 2.3.8 Testing robustness: Argentina defaults 2.3.9 The end of the self-fulfilling prophecy The growth of local currency debt Why invest in emerging markets?

42 44 44 46 47 48 51 53

3 The 2008 Credit Crunch and Aftermath 3.1 Bank regulation failure 3.1.1 Sub-prime 3.2 The 2008 crisis 3.3 Depression risk 3.3.1 Reducing the debt 3.3.2 Deleveraging is not an emerging market problem 3.4 Global central bank imbalances

55 58 60 63 64 66 67 71

4 Limitations of Economics and Finance Theory 4.1 Theoretical thought and limitations 4.2 Economics, a vehicle for the ruling ideology 4.3 Macroeconomics 4.4 Microeconomic foundations of macroeconomics 4.4.1 Efficient market hypothesis 4.4.2 Modern portfolio theory 4.4.3 Investment under uncertainty 4.5 Bounded decisions and behavioural finance

77 77 78 79 81 84 87 88 90

5 What is Risk? 5.1 Specific and systematic risk 5.2 Looking backwards 5.3 Uncertainty 5.4 Risk and volatility 5.5 Risk in emerging markets 5.6 Rating agencies 5.7 Capacity, willingness, trust 5.7.1 Rich countries default by other means 5.7.2 Two sets of risk in emerging markets 5.8 Sovereign risk: a three-layer approach 5.9 Prejudice, risk and markets 5.9.1 When you have a hammer, everything looks like a nail

95 99 103 104 105 106 108 109 110 111 114 116 117

6

119 120 121 124 124

Core/Periphery Disease 6.1 The core/periphery paradigm 6.1.1 Core breach? 6.1.2 Another core/periphery concept: decoupling 6.1.3 And another: spreads

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Contents

6.2

Beyond core/periphery 6.2.1 Towards a relative theory of risk 6.2.2 GDP weighting

vii

125 125 126

7

The Structure of Investment 7.1 Misaligned incentives 7.2 Confused incentives 7.3 Evolutionary dynamics, institutional forms 7.3.1 History matters 7.4 Network theory 7.5 Game theory 7.6 Investor structure and liquidity 7.7 Market segmentation 7.7.1 Warning signals 7.8 Investor base structure matters

131 132 134 135 137 138 139 140 142 144 147

8

Asset Allocation 8.1 Asset classes 8.1.1 Alternatives 8.2 How asset allocation occurs today 8.2.1 Investor types 8.2.2 Asset/liability management 8.3 From efficiency frontiers to revealed preferences 8.4 Asset allocation vs manager selection; active vs passive 8.5 Allocating at sea

149 151 154 155 156 158 161 164 167

9

Thinking Strategically in the Investment Process 9.1 Thinking strategically 9.1.1 Thinking strategically: appropriate discounting 9.2 Scenario planning 9.3 Global structural shifts ahead? 9.3.1 Asset allocation: some proposed new rules 9.4 Investment process in emerging debt 9.5 Conclusion

169 169 169 170 171 172 174 177

10 A New Way to Invest 10.1 Sense-checking assumptions 10.1.1 Risk, uncertainty and information asymmetry assumptions 10.1.2 Investor psychology and behaviour assumptions 10.1.3 Structure, market efficiency, equilibrium and market dynamics 10.1.4 Asset class definitions 10.2 Assessing liabilities 10.3 Your constraints 10.3.1 The decision chain 10.3.2 Institutional capabilities 10.3.3 Psychological constraints 10.4 Consider changing your constraints: agency issues

179 180 181 185 187 188 189 190 190 191 191 192

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Contents

10.5 10.6 10.7

Building scenarios Understanding market structure Asset allocation 10.7.1 Route 1: Comprehensive 10.7.2 Route 2: Entrepreneurial 10.7.3 Asset allocation dynamics 10.8 Meta-allocation: toolset choice 10.9 Follow the skillset 10.10 Portfolio construction and monitoring 11 Regulation and Policy Lessons 11.1 Regulating financial institutions: new and old lessons 11.1.1 Fix the banks 11.1.2 Non-banks: who holds what? 11.1.3 Reduce agency problems: trustee incentives 11.1.4 Honour public service 11.1.5 Choice architecture 11.2 What to do about systemic risk? 11.2.1 Avoid regulation that amplifies risk 11.2.2 Beware market segmentation 11.2.3 Structure matters 11.2.4 Map perceptions of risk 11.2.5 Detect and stop asset bubbles 11.2.6 Preserve credibility 11.3 Wish list for emerging market policymakers 11.3.1 Allow markets to work 11.3.2 Proclaim and foster greater pricing power 11.3.3 Promote EM global banks, south-south linkages 11.3.4 Build capital markets 11.3.5 Fight core/periphery disease 11.4 Reserve management and the international monetary system 11.4.1 The dollar is your problem 11.4.2 Alternatives to the dollar 11.4.3 Too many reserves 11.5 What investors can expect from HIDC policymakers 11.5.1 Financial repression 11.5.2 Consequences of financial repression for banks 11.5.3 No early exit from quantitative easing? 11.5.4 Bond crash 11.5.5 Inflation 11.5.6 Appeals to foreign investors 11.5.7 Regulatory muddle-through 11.5.8 Pension reform 11.5.9 Pension regulatory conflict may only abate once EM investors exit 11.5.10 Rating agencies 11.5.11 Intellectual reassessment 11.6 What investors can expect from emerging market policymakers

193 194 195 196 197 198 199 200 201 203 204 204 206 206 207 208 208 209 210 210 212 212 213 213 213 214 214 216 216 217 217 218 221 222 222 223 223 224 224 224 224 225 225 225 225 226

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Contents

ix

12 Conclusion 12.1 A final list… 12.2 … for an upside down world

229 229 231

Further Research

233

Disclaimer

235

Glossary

237

Bibliography

245

Index

257

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1 Globalisation and the Current Global Economy ‘Globalisation should not be just about interconnecting the bell jars of the privileged few.’ De Soto (2000, p. 219) ‘The benefits of globalization of trade in goods and services are not controversial among economists. Polls of economists indicate that one of [the] few things on which they agree is that the globalization of international trade, in which markets are opened to flows of foreign goods and services, is desirable. But financial globalization, the opening up to flows of foreign capital, is highly controversial, even among economists …’ Mishkin (2006) In this chapter we discuss some of the background to attitudes towards money and debt. We explore the historical erosion of despotic power. We aim to understand globalisation, both ancient and modern, identifying the trends most important for current events and policy actions.

1.1 What is globalisation? Globalisation has been through a number of cycles including in the nineteenth century period of free trade.1 The term requires careful use: its range and ambiguity in common parlance can cause misunderstanding. Globalisation is both a state and a process. It has an economic facet, perhaps best described as the greater interconnectedness of trade and investment as transactions costs and barriers reduce, but also the idea of lack of constraint on markets by government. It is this element that investors are ultimately most interested in. But globalisation also has a technological aspect (not distinct from the economic aspect), notably as represented recently by innovations and speed in modern communications. And it has a cultural facet, as exposed in the spread of common means of entertainment and ideas. It homogenises, and through standardisation commodifies, but also creates diversity of choice; complicates and simplifies; brings benefits and conflicts; produces winners and losers. For emerging markets today, globalisation accelerates convergence to the living standards of developed markets. For some, in a world in which the voices of those with something to lose are louder than those who gain, the term is laced with emotive content, often negative, and while it creates jobs and wealth, globalisation is, for many, associated with job losses and erosion of local values. The term has many definitions and while Findlay and O’Rourke (2007, p. 108) argue that globalisation may have begun with the Mongol unification of the Eurasian land mass, that process is a distant shadow of what is called globalisation today. 1

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Emerging Markets in an Upside Down World

Ridley (2010) argues that, among other things, it was trade which propagated innovation, technology and civilisation, as do Findlay and O’Rourke (2007). Jane Jacobs in her book Systems of Survival (1992) uses a Platonic dialogue to describe the different logics of politics, which is a zero-sum game, and commerce, a positive-sum game. Nations have historically competed for scarce territory, which, if one gained, another must lose; whereas both parties gain from a trade freely entered into. If we want to avoid conflicts, commerce has a positive role to play. It is no mere historical coincidence that periods of protectionism and limited international trade often precede wars. The failed logic of isolationism and of fighting over land and other limited resources leads from mercantilism to gunboats, to strategic invasions of third countries, and to empire. Empires fall, however, or at best are managed into relative decline. The human tendency to barter and trade is certainly older than recorded history, and has long been geographically broad in extent. Though this is a generalisation, in the progress of greater economic interconnectedness there are waves, affected by human policy and history, as well as trends, driven by technology. Wars of the ‘hard’ and trade variety, restrictions on economic activity and trade, mass migration and natural disasters have all been disruptive but also sometimes stimulate innovation and new forms of economic activity. In contrast, political stability and incentive structures compatible with innovation have generally nurtured both existing patterns in trade and globalisation. Braudel (1998) in his history of the ancient Mediterranean world argues that early transhumance (seasonal migration between summer and winter pastures) established a pattern of seasonal trade in the region. The world has clearly seen ebbs and flows in the extent of trade links and civilisations. Toynbee (1946) and others have categorised the rise and fall of civilisations, and with them trade and international links. The story of lack of stability wreaking havoc on economies and trade has repeated itself many times. Globalisation can and does ebb as well as flow. Technology has reversed on occasion as inventions have been forgotten once civilisations collapse. Our European Renaissance is in name a rediscovery. Arguably, however, it takes the destruction of civilisation to reverse technology, and in the modern era, as during periods of stability within earlier civilisations, it has been tenacious and non-reversible. Technology profoundly impacts globalisation. It can aid economic growth, productivity and, by reducing transport costs, trade. Technology, by changing relative prices, also changes our institutions, as Douglass North (1990) has taught us. For example, technology effects disruptive upheavals in communication from time to time. Neil Postman (1985) has described how the written word, printing and then newspapers changed the pace and nature of interconnectedness. For example, in the 19th century, the telegram helped create the commercial success of newspapers and their news – the interest and novelty of new information from a long distance being of interest primarily, if not solely, because it was new. This ‘news’ content eroded and then eclipsed more considered thought, telescoping cultural knowledge and political debate to focus more heavily on the near present. Thus with the telegram came the modern newspapers, and with them came trivia, including the invention of the crossword puzzle – to test the reader’s knowledge of news trivia. Subsequently broadcasting has impacted our communication patterns: for example, in the 1850s US presidential candidates would deliver speeches several hours long in public debates, long enough to justify meal breaks. That voters would spend the time to listen to such debates, and that they could comprehend the complex structured paragraphs which were so characteristic, stands in stark contrast to the norm of exchange so typical today.

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Globalisation and the Current Global Economy

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Have we since ‘dumbed down’, and does the process of globalisation contain a series of dumbing-down episodes? Not entirely: the telegram, newspapers and then radio and television created a breadth of participation in culture not previously experienced. Political debates became less elitist and more inclusive, with more elite communication and interaction continuing, but less dominantly – less unchallenged. The perception of dumbing down, and indeed the collapse of our sense of history to a more myopic immediate past, is clearly a strong one but not just a 19th-century one. The impact of television is an issue studied by Postman and especially in the post-Second-World-War US context by Robert Putnam (2000). His book Bowling Alone created a vigorous debate about whether television has been the main factor behind the postwar decline of US civic culture (an example of which has been the decline of community bowling alleys). Cries of ‘Dumbing down!’ go all the way back in history. The move from the oral tradition to the written word was lamented in ancient Greece and seen as destructive of the memory skills developed in the time when Homer’s ‘Iliad’ and ‘Odyssey’ – arguably still the greatest epics of literature – were related by word of mouth.2 Broad access to books, particularly the Bible in 16th-century Europe, facilitated religious revolution and war. Dumbing down may look sacrilegious to an elite,3 but while it may represent a destruction of the means of valuable interchange of ideas, at the same time it can be revelatory and intellectually enriching for many more people not previously communicating with each other. Technological change in the media has been not only traumatic but also irresistible as old technologies have been replaced. It affects the way newer generations think and interact. There is a feeling of erosive unstoppable destruction of the old as globalisation, via new media, invades. New technologies and the young bring myopia and collective amnesia. Older generations and traditional societies alike feel the tension as their children and communities adopt new modes of speech and ways of thinking and abandon the past. And this is not new. We may fear (or embrace) such change but we can’t credibly blame (or give all the credit to) our children. Globalisation may be a more convenient and acceptable receptacle for our emotional discomfort. Modern communications have massively increased information flow, and the technologies of the Internet and mobile phone have leapfrogged older technologies in many developing countries.4 As with technological change before, much of this is inexorable and non-reversible. Knowledge of the wider world and aspirations for a better life combine in emerging countries to increase awareness. As populations become more vocal, this leads to pressure on elites for political and economic reform at home. Economic growth and international competitiveness is in part the result of greater entrepreneurial opportunities. Others leave and migrate to the developed world, competing in labour markets there. Either way, the result is greater economic competition with the developed world, which either has to adapt or face job losses from increased competition. Thus many in the developed world feel threatened by globalisation, while at the same time it opens vast new opportunities to many in developing economies. Part of globalisation is significant international trade, and also substantial cross-border flows of factors of production (capital, labour, technology). These flows take advantage of See also Gleick (2011) p. 48 for more recent examples of complaints about the loss of oral culture. See for example Judt (2010, p. 172): “In the US today, town hall meetings and ‘tea parties’ parody and mimic the 18th century originals. Far from opening debate, they close it down. Demagogues tell the crowd what to think; when their phrases are echoed back to them, they boldly announce that they are merely relaying popular sentiment.” 4 See for example Jeffrey, R., and A. Doron’s The Great Indian Phone Book (2013). 2 3

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Emerging Markets in an Upside Down World

pricing differences, but in the process, also help reduce them – globalisation helps move the global economy towards an equalisation of returns to factors of production. It also involves multilateral production, and with it the spread of ideas and technology. There is greater openness to foreign inward investment. There is competition between governments for that investment, and for the jobs and knowledge which come attached. Different parts of the same production process may take place in several countries, exploiting comparative advantages. This is made possible by sufficiently low levels of protectionism and reliable low cost transport.

1.2 Economic history and globalisation To concentrate on the economic facets of globalisation, it may be useful to consider its historical precedents. Large-scale globalisation is not new. Maddison5 argues that ‘[i]n proportionate terms, globalisation was much more important from 1500 to 1870 than it has been since. A great part … due to gains from increased specialisation and increases in the scale of production’. International trade and capital flows are much larger today, but so is the global economy. There was an interruption as the world went to war in the 20th century, and then protectionism was only reduced gradually, but globalisation is clearly not a novelty. There are also long economic waves of concern with inflation and deflation. Allen (2005) for example argues that the inflation of the 1970s was partly born from the concern over employment that had previously dominated since the Depression, and similar long waves have been picked by many others since Kondratiev (1925). Kaplinsky (2005) makes a comparison between the late 19th and early 20th century period of internationalisation on the one hand, and on the other hand the period of globalisation starting in the late 20th century. He comments on the different mix of goods traded, and on the different migration patterns – of the poor in the earlier phase and the skilled and a greater proportion of the monied in the latter.6 He also points out7 that there is a high correlation between effective financial intermediation and economic growth, but that excessive volatility can reduce growth. Hence policymakers often want the competition, ideas and capital which come with openness but are concerned about volatility. Although portfolio investors are not necessarily short term in their outlook, some policymakers – not liking potential volatility in their exchange rate driven by short-term changes in cross-border portfolio flows – are attracted to the idea of discouraging portfolio flows through taxing capital inflows rather than trying to attract more long-term stable investors. Yet trying to prevent inflows rarely has more than a temporary impact on the exchange rate; given inevitable efforts to bypass the restrictions, it can encourage speculative pools of capital and discourage longer-term flows. Hence the simple mantra that characterises

Maddison (2007) pp. 78/9. He also asserts that the recent period saw short-term as opposed to long-term capital flows. Yet, although the speed at which financial transactions can occur has quickened, there were in both periods crises involving short-term bank loans as well as longer-term bonds. Moreover, after the Brady plans and certainly since the Russian crisis of 1998, as we shall relate in more detail in Chapter 2, the dominant investors in emerging debt moved from those with more speculative motives to those with long-term liabilities making strategic allocations. Markets have remained liquid and on occasion volatile, but both the bonds and the timeframes of the investors are now longer term. 7 p. 78. 5 6

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Globalisation and the Current Global Economy

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portfolio flows as speculative and thus undesirable may be misplaced. Indeed, efforts to restrict such flows can result in more not less volatility.8 1.2.1

the desire to control and its impact on trade

Why do simple policy measures to reduce volatility so often backfire? Today’s environment is one of economic complexity, economic liberty and freedom of thought and action. There is a lot of uncontrolled international movement of goods and capital, whereas in the preindustrial past the movement that did exist was smaller and simpler. The state may have become less able to impose direct control on the mass of individuals and firms, but many have also become more sophisticated at indirect control and at exploiting the behaviour of firms. As Lucas (1976) pointed out in the so-called ‘Lucas critique’, the use of aggregate macroeconomic data to predict the effect of policy changes can be frustrated. This is due to the behaviour captured by such aggregate data not being independent from policy, but affected in complex ways. Political control in many spheres has changed. It has been decentralised in some cases as smaller groups have asserted themselves and the centre become less powerful, such as during the fall of Rome in the 4th century, but also due to deliberate delegation of responsibility downwards. In other spheres power has centralised, and many of the problems faced by policymakers today require action above the level of the nation-state to be effective, including some aspects of anti-terrorism and environmental policy. As people’s identities and loyalties have multiplied and become more complex and global, identification with the state has also changed, and the degree to which countries can co-opt their citizens in certain ways. But democracy and well-being are both probably strengthened by this greater complexity. The multiplication of special interests, competing with each other, reaches a point beyond which any central source of political power can command a majority of support whatever mix of policies is chosen. Democratic institutional forms constitute instead legitimising filter mechanisms, the function of which is not merely to create compromises between political groupings but also to allow all politically active groups and individuals to accept and abide by these compromises. Such decentralisation of power is incompatible with authoritarianism. Authoritarian governments fail when their populations no longer acquiesce to their policies. Today, the freedom of action which comes from the failure of totalitarianism and central power through filter mechanisms such as democracy, erodes national boundaries and creates more scope for globalisation. Competition of ideas and in markets aids creativeness and wealth production. Let us cast our minds back to medieval Europe, and in particular England. A useful working hypothesis for any government is that it strives to maximise revenue in order, in turn, to maximise power.9 Medieval monarchs needed revenues for wars, and could often justify taxes to finance them. How they managed this is instructive for how economies, international trade and capital markets developed. A characteristic of England’s history is that the king’s power, being weaker with regard to the aristocracy compared with that of the king of France, had greater need to legitimise taxation. The Magna Carta of 1215 limited King John’s and Li and Rajan (2011) conclude from an empirical study that controls on equity inflows tend to raise the volatility of those inflows, and controls on FDI (foreign direct investment) and also on debt outflows may both increase the volatility of equity outflows – substitution effects. Controls on FDI inflows may also raise the volatility of FDI outflows. Controls on debt inflows tend to raise the volatility of those inflows. See also Frenkel et al. (2001). 9 See Levi (1988). 8

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Emerging Markets in an Upside Down World

subsequent monarchs’ powers (weakening under the Tudors and Stuarts in particular), establishing personal and property freedoms and elevating the rule of law above the will of the monarch. Subsequent efforts to raise taxes were notoriously difficult, but this led to an ironic reversal. ‘The relatively weaker bargaining position of English monarchs vis-à-vis their constituents led to concessions that French monarchs did not have to make. However, the Parliament that evolved ultimately enhanced the ability of English monarchs to tax. Parliament provided a forum for conditional cooperation. It engendered quasi-voluntary compliance and reduced transactions costs.’ Levi (1988) Compared to 18th-century France or Rome under the later Caesars, tax farming in England was not widely employed, but rather the taxes collected by Parliament and later the bonds issued involved lower transaction costs, were more legitimate and more reliable. The range of taxes to finance the monarchs (and their wars) was varied, but was also driven by and impacted the pattern of trade. Taxes needed to be collectible with minimum transaction costs and maximum legitimacy, and hence moved from general levies (amounts collected across the population) to consumer goods to trade to income. But strategic and mercantilist concerns over trade led to developments in policy too. In the mid-16th century the focus of English trade policy was to generate employment and food after the devastating costs of war on the continent (the English were at war for much of the previous 50 years, with a break in the 1530s). The discouragement of domestic production of luxuries including luxury clothing, seen as unnecessary and sapping of the national economy, led to surges of some of these items as imports, and so eventually the reversal of the original trade policy.10 Then mercantilist and strategic concerns regarding foreign trade started to give way in the later 17th century to the appreciation that ‘projects’ (schemes of domestic investment for home consumption) were important for the country’s overall income and economic health.11 Patents, starting from the Tudors (the first in 1552 for a technique for making glass, then in 1554 to search for and work metals in England), were established to promote production but often led to the aristocratic holders of such exclusive licences closing down domestic (and less aristocratic) competition. International trade was a small share of the total economy, but it was also clearly impacted by the dominant position of government policy in the national economy. We can say there were periods of great increase in foreign trade, but it was still very much monitored and to a large extent controlled or controllable by governments. Governments in turn acted for a combination of political, strategic and revenue-maximising ways, both directly and through taxes, distorting the incentives to trade. Today’s economic freedoms contrast with more restricted governance structures dominated by guilds and serfdom, tariffs and trade restrictions, economic dependency and personal immobility, and general government heavy-handedness. Whereas the norm in medieval Europe was that companies would seek a licence to engage in certain activities, now companies are prevented from not doing certain things – i.e. they can do anything else. Over time,

Sir Thomas Smith listed various imports in his Discourse of the Commonwealth, and then sat on a committee in 1559 which framed plans for legislation to promote import substitution for a similar list of goods. 11 As described by Joan Thirsk (1978). 10

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Globalisation and the Current Global Economy

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governments have become less able to ignore the wishes of their citizens. And this is true globally not just nationally. 1.2.2

the influence of money

While our focus is economics, other social forces have also constrained and shaped economic activity. If importation of luxuries was long seen in medieval England as a distraction from more legitimate economic activities, attitudes to money as the medium of exchange take centre stage in the battle between God and Mammon. The history of money is fascinating, as is its sociology and philosophy.12 The association of money with moral impurity is a common thread from Judas Iscariot to the laws against usury and right up to the present day. In Christian Europe at least, financial market development was restricted by religious mores. However, monarchs still needed to fight, and that cost money. The first banks began in the 15th century, and international loans commonly funded wars between monarchs. Potosí silver fuelled the wealth and power of 16th-century Spain, but the lack of development of a domestic capital market led to Spain under Philip II defaulting again and again to foreign bankers. Florence, Genoa and Amsterdam built their economies on international loans as well as international trade. Banks were originally a place to secure one’s money. Once they started lending out more than they had through issuing notes, there was a risk of bank runs… and the bigger the bank, the bigger the run. Though the Bank of England (1664) and Sveriges Bank (1668) were already established, the mass creation of credit proposed by John Law to the French government, in effect creating a central bank, was initially rejected in 1715, even though the French government was near default following the War of the Spanish Succession (1701–1714) and the Sun King’s defeats at the hands of the Duke of Malborough and Prince Eugene. However, Law was allowed to establish the Banque Général, a private bank allowed to issue bank notes in place of scarce gold and so stimulate the economy. The bank in effect became the central bank, and from there Law’s scope for credit creation grew and grew in an 18th-century version of our modern-day quantitative easing (QE). Initially providing assistance in financing government, by 1717, Law’s notes were legal tender for paying taxes. The attraction of printing money and credit extension became apparent as a giant means of financing government. Depositors and equity holders came to trust in paper returns. The bank also became an investor in the Mississippi, and shares in the bank were bid up in a financial bubble fuelled by promises of profits which were not forthcoming. It all ended in tears with one of the largest bubble-bursts in history in 1720; but for about 15 months this Scotsman, made Duke of Arkansas but wanted for murder in England,13 was the most powerful man in France. Love of ingenious financial alchemy (the successful stimulus to the French economy by the initial period of credit creation) was followed by hate (the bursting of the Mississippi speculative bubble); just as today international bank alchemy (a key element of modern globalisation) can create huge benefits but then excessive leverage and crises, followed by public opprobrium.

See for example Dodd (1994), Buchan (1998), Galbraith (1975), Shell (1982) and Simmel (1990). John Law initially fled to Scotland to escape justice, and lobbied against the Union with England, but then had to flee Scotland when the Act of Union (Scotland with England) was passed in 1707. 12 13

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Emerging Markets in an Upside Down World

The closeness of bankers to political power continues to the present day.14 The Rothschilds’ agent in 19th-century Berlin, Gerson Bleichröder, became Bismarck’s personal banker but also was central to the finances of the German state and even to foreign policy. Bleichröder provided Bismarck with backchannels via the Rothschilds to the government in Paris and to Disraeli in London. He also was heavily involved in foreign investment, particularly in railways (see Stern, 1977). Likewise in the 20th century, John Pierpont Morgan was famous for playing the role of domestic central bank, stopping the US financial panic of 1907. The associations between finance, international relations and globalisation have long been strong. 1.2.3 trade and commodification International trade enables international specialisation. In Britain’s case it was the colonies and the ability to import food and raw materials which enabled the industrial revolution. One needs political stability and trust for international trade and globalisation, and trust is personal and built on reputation – hence the growth of family partnership merchant banks, with their own money on the line. These banks had detailed knowledge about others; but, as they lived or died on their reputation, which could be shattered by a single scandal, they kept secrets well. Globalisation also entails creating the demand for international trade. Trade has existed for centuries, but large-scale trade had to wait for the consumer society. Prior to this, trade was either in luxuries for the few, or in one or two goods for the many. Roman imports of grain from North Africa were clearly considerable in scale, and a staple for the urban population, but the more normal pattern has been of relative self-sufficiency in most staples until the past few centuries, when large-scale trade in grain and textiles resumed. Trade built over several centuries in Europe, but important steps on the way were recognition from the late 17th century of the importance of domestic demand; increasing rural industry and incomes; agricultural enclosure and labour specialisation; and urbanisation. Commodification – assigning economic value to things not previously so considered – was also a crucial step. London’s Great Exhibition of 1851 was a triumph for the establishment of the commodity at centre stage. The exhibits were not explicitly for sale, but rather there for the glorification of industry and the production of items – commodities from soap to tea to heavy machinery – of use to the consumer and society. This was a revolutionary change and to a large extent the attraction of the exhibition: the focus of the commodity from derivative to dominator of human relations (and not just economic relations). Modern advertising and branding were born, and in some of the pictorial advertisements of the time people were in clearly subservient (smaller and not as important) positions to the commodity.15 Complementary to this radical change was the British Empire, acquired as if by accident for an unknown purpose, but now perceived as having an important role in supplying the growing needs of the consumer (even if the Americas in practice were more important in this role). If the Great Exhibition drove the desire for domestic consumption, truly dominant mass consumption had to wait for Henry Ford in the US during the early 20th century.

In Atlantic Monthly May 2009, Simon Johnson, Former IMF Chief Economist, referred to this bankingpolitics nexus as a “financial oligarchy that is blocking essential reform” http://www.theatlantic.com/magazine/ archive/2009/05/the-quiet-coup/307364/ 15 See Richards (1990). 14

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Commodification continues to this day and is a distinctive part of globalisation, combined with its offspring, advertising and branding. 1.2.4

nationalism

Interaction between nations has also changed. Nationalism is a fairly new concept in its modern sense, with nation-states developing in the 1700s, as Hobsbawm (1990) has pointed out. It may yet, as a result of globalisation and the political consequences of societal complexity already mentioned, give way to more multinational political structures. It has already changed greatly. No longer (with a few exceptions) is it an extension of a monarch’s ego: ‘L’état, c’est moi’ as Louis XIV, the Sun King, is supposed to have described it. The peace of Westphalia in 1648 after the devastating Thirty Years’ War between Catholic and Protestant forces defined the sovereign state and established the principle of non-interference in the affairs of other sovereign states. But as Philip Bobbitt (2002) has described, the state has been through several stages of development since. Most recently, as the 1990s Balkan wars demonstrated, the concept of non-interference established in 1648 is now in conflict with that of self-determination. Having said that, global interference has always been with us: stronger states interfering in the affairs of smaller ones for a combination of reasons: their own (individual or collective) security, economic self-interest and humanitarian aid. In all but the short term, however, self-interest of some description invariably dominates.16 What global media and culture have aided is the move to a new reality in which the winning of hearts and minds is central to the modern strategic battleground, and in which traditional armed forces are largely redundant. As a population becomes more educated it will organise and will demand political rights: its voice17 will be heard and authority perceived to be unjust becomes more difficult to preserve. The days when the 1000-strong Indian Civil Service of British expatriate administrators could run a subcontinent of 300 million is long gone, as was predicted since the British enhanced education for Indians from the 1830s. The values the British chose to spread in India were incompatible with their longer-term presence. Today the spread of these and similar values makes similar long-term passive subjugation of nations quite impossible. In today’s world of the Internet and global media, guerrilla not industrial warfare, mass political participation not autocracy, control by physical force alone has become absurdly difficult – even if this is not yet sufficiently understood to have prevented some recent attempts. Winning minds is the clear preferred route to stable prosperity in today’s world, with a more limited support role reserved for physical force. This is in contrast to much of the structure of defence spending, as discussed by two modern generals with recent experience in the Balkans: Clark (2001) and Smith (2005). What has changed is not merely our education and communications technology, but the number of independent countries18 and also the principle of non-intervention in what were previously considered the internal affairs of nation-states. The principle of such non-interference is now in conflict with the desires, often supported by international opinion, of certain sub-national groups for self-determination. The nation-state So-called humanitarian imperialism is not particularly credible as a justification for armed intervention in a foreign country for the simple reason that it tends not to work. 17 See Hirschman (1970). 18 The increase in the number of countries has made international policy co-ordination more difficult. This fragmentation has been the result of the end of empire, decolonisation and more self-determination. These processes in turn have been assisted by globalisation and with it the spread of economic liberty, education and liberal values. 16

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Emerging Markets in an Upside Down World

has changed its form several times before. As nationalism is a relatively modern phenomenon in many ways, one should expect it to change further.19 Jane Jacobs describes politics as a zero-sum game, but maybe it is even worse: due to fragmentation and more issues requiring international co-operation, states are experiencing shrinking power. Some international problems which would have been resolved by a few countries in the past are now not being resolved. In the wake of globalisation, national politics are becoming less and less autonomous. A number of immigration, environmental and economic issues require supranational governance. In the face of these issues, and in the absence of powerful international institutions, national governments are becoming more impotent; and electorates, realising this, are becoming more frustrated that issues are not being resolved – more apathetic (as shown by voter participation trends), more difficult to please. Winning minds is about having people agree with you, after letting them freely choose to do so. For the West this freedom or empowerment means allowing people to run their own lives, but also giving up some power – including sharing responsibility with emerging markets. Much more serious reform of the voting shares of the IMF, still heavily dominated by the US and Western Europe, would be a start. The problem is that some Western politicians have great difficulty with implementing this, or giving up their influence, especially when they have little central collective leadership, or are leaders with outdated world views. They often seem oblivious to the observable reality that their policies are getting in the way. Investors, the ultimate pragmatists, have the potential to offset the zero-sum (or all too often negative-sum) logic of politics. Commerce is an important component in bridging conflicts and avoiding them, of getting over ideological prejudices, of creating mutually aligned incentives: in short, of keeping down the testosterone levels in politics. History can teach us a lot about globalisation but we have to be cautious in interpretation. A reader may misinterpret the values and motives of past decision makers. How do we square Britain’s great historical achievements with urban squalor, Irish famine in the 1840s, and Orwell’s description of waning empire in Burmese Days? Much of the past has been horrific, and governments have gone to great efforts to rewrite and in some cases to deny history20, but we also need to recognise that moral values change over time and geography.21 And values remain different in different geographies today, even if people in distant lands consume some of the same brands and superficially may appear very similar in their values. Also, in the midst of global economic dynamism, there can still be a tendency to be surprised, antisympathetic and hostile to change. The combination of remaining partially myopic yet more interconnected leads to greater risk of sudden uncomfortable change, and indeed conflict.

1.3 rEcEnt globalisation International trade often collapses during war, and economies have struggled with inflation and debts in peacetime. Hence the importance of local and global rules to facilitate trade and capital flows – to facilitate globalisation. Stability matters, as Maddison (2007, p. 111) writing about the period 1500–1800, points out:

See Bobbitt (2002) and Hobsbawm (1990). See for example Paris (2000). 21 See Harris (2010) for an interesting exposition of how science can determine morality. 19 20

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‘In the UK and the Netherlands, the legal system protected commercial property rights and ensured the enforceability of contracts. State levies were predictable and not arbitrary, and credit for long term ventures was available. As a result groups of capitalists in these countries were able to establish corporations like the Dutch Far East Company (VOC), and the British East India Company (EIC) which could organise risky ventures over huge distances.’ The Bank of England had established the model for central banks up to the 19th century, balancing liquidity needs in the economy with the avoidance of inflation;22 and the gold standard had emerged to create stability in foreign exchange transactions, with major currencies convertible to gold. This facilitated global trade. The gold standard, however, was vulnerable to supply shocks as new gold deposits were discovered and, in times of war and crisis, countries abandoned it. The Treaty of Versailles in 1919 imposed unrealistically high costs on Germany, which (as John Maynard Keynes warned in The Economic Consequences of the Peace, written in 1919) later forced an exit from the gold standard as their debts became unpayable (and unpaid for their creditors), and set the scene for hyperinflation. 1.3.1 bretton Woods The roaring twenties and the stock market boom of 1929 were followed by deflation and depression and then World War II, during which trade patterns were radically curtailed and war economies operated in more planned fashion. Given the strong desire to avoid the inter-war protectionism, and the periods of both deflation and then inflation, in 1944 at Bretton Woods a new global monetary architecture was designed. The International Monetary Fund (IMF) was created to police exchange rates. The main arm of the World Bank, the International Bank for Reconstruction and Development, was tasked to help rebuild Europe. An international trade organisation failed to be agreed to, but belatedly in 1947 the General Agreement on Tariffs and Trade (GATT) was established instead to promote multilateral trade, replaced by the World Trade Organization (WTO) in 1995. An objective was to start to reverse protectionism and promote current account convertibility. The agenda at Bretton Woods after the inter-war deflation was thus to re-establish some form of managed gold standard. However, there was not enough gold in the world (without a major and geopolitically unacceptable price increase which would have benefitted Russia and South Africa) for full convertibility. Keynes conceived that all foreign exchange transactions would continue to go through central banks, as during the war, and suggested a new international currency called ‘Bancor’ which would be the unit of account for international transactions, expressed in units of gold. A new international clearing union would facilitate transactions and prevent the build-up of major imbalances. Keynes’ design was rejected by the US. This was because, as a major creditor, the US decided not to have penalties imposed on her Bancor surpluses: countries running excessive trade deficits are eventually penalised by being cut off from capital as their credit-worthiness deteriorates, but for every deficit there is necessarily an equivalent surplus somewhere,23 and so to avoid the build-up of deficits Keynes also proposed that interest be charged on excessive Bancor surpluses. See for example Galbraith (1975) for a history of the development of the banking system and central banking. Though it is to be noted that for a well-functioning gold standard the converse is not necessarily the case: while there is a surplus for every deficit, there is not always a deficit for every surplus. 22 23

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Emerging Markets in an Upside Down World

Instead of Keynes’s plan, a system with an inbuilt tendency towards imbalance was implemented, and still (in 2014) codified in the ‘Articles of Agreement Establishing the IMF’. Currencies had fixed exchange rates to the US dollar, which in turn was convertible to gold at $35 an ounce (the official US Treasury price since 1934). Though other countries could have also chosen to have their currencies convertible to gold, none did. Other major currencies were pegged to the dollar. This led to the so-called Triffin dilemma: a national currency, which is also the international currency, has conflicting pressures of attaining short-term monetary balance nationally and longer-term international balance. Currency and yielding government securities are issued to meet international demand. Otherwise international trade and growth would fall. This, however, creates a tendency towards trade deficits for the issuing country, as the money is printed there and then makes its way into the international economy through being used to purchase goods and assets (as opposed to a more international currency which might be distributed, on printing, more evenly across countries). This in turn builds US debts to the rest of the world. In order to prevent requests that these claims be converted to gold (this leading to an eventual breakdown of the system), the US would either have to raise interest rates on the government securities issued and which are held by foreigners; or to tighten fiscally in order to shrink the economy and its indebtedness, or otherwise reduce the trade deficit. There is an understandable reluctance to do either. They could (as Triffin observed) also allow the price of gold to rise, but this was outside the Bretton Woods agreement and could create destabilising speculative moves into gold by both central banks and private investors – in turn draining US gold stocks and undermining the ability to intervene, and so faith in the reserve system. The desire by foreigners to offer their goods in exchange for one’s currency (in the case of the dollar) can be an attractive alternative to greater domestic fiscal discipline. If, however, there is a tendency to fiscal indiscipline or substantial overseas expenditure, there will be more debt growth relative to the gold supply (eventually reducing the credibility of the promise of convertibility to gold). The problem is exacerbated if the international economy outside the US expands faster than that of the US: then international demand for dollars also expands faster than domestic demand for dollars. At the end of the war, this system, initially assisted by the Marshall Plan, helped create global stability and the rapid re-industrialisation of Europe and Japan – the (re-)emerging countries of the day.24 With the US dominating global tradable goods production, European countries bought US goods with the US dollars from the Marshall Plan. The recovery of these economies was successful. The pattern of strong growth in Europe and Japan then continued after the Marshall Plan and the initial period of US surpluses ended. By the early 1960s Europe and Japan had become highly competitive. The fixed exchange rate facing the US had become inappropriately high. The move to current account convertibility in 1958 had put additional pressure on the dollar, as had European concerns about the fiscal policies of the newly elected US president Kennedy. The dollar traded as low as $40 an ounce in 1960. The response was the London gold pool: an agreement whereby the US and seven European central banks co-ordinated sales of gold in the London gold market, buying dollars, which they then invested in US Treasuries. This was no more than a temporary fix, though. In this monetary system the US in the immediate postwar period was doing the rest of the world a

24

The term ‘re-emerging markets’ is from Wolf (2009).

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Globalisation and the Current Global Economy

21

huge favour by creating demand for their goods, but at the cost of building up more and more debt.25 As Herb Stein, US President Nixon’s adviser at the time, said: ‘If something cannot go on forever, it will stop.’ Add the cost of the Vietnam War and the unpalatable choice of fiscal adjustment through higher taxes, and the pressure grew. The more aggressive stance by the incoming Nixon administration towards Europe over gold purchases, and specifically the rhetoric of his Treasury Secretary John Connally, backfired and led to market pressure on the gold price. The German mark was floated in May 1971 and then on August 13th the Bank of England asked the US to convert some of its dollars to gold before it was too late. Nixon decided instead to de-link from gold on August 15th, devaluing the dollar, which then rapidly fell from $35 an ounce to $44 an ounce. As the dis-equilibrium had grown to such a level, there was indeed little else he could have done.26 Sterling floated in June 1972 and some other currencies still pegged to the dollar until early 1973, but the credibility of their anchor had been damaged. Setting a precedent for the more recent build-up in global imbalances in the early 21st century, it was the action of surplus central banks which forced the 1970s crisis. US debts were far in excess of gold reserves. Foreign surplus central banks, wanting to preserve the purchasing power of their reserves, started buying back gold on the secondary market, and eventually asked for gold from the US Treasury in exchange for dollars. Nixon then had a choice: start giving out the gold, run out and then renege on the commitment to convertibility; or renege early and keep the gold. He quite sensibly chose the latter option, although technically another option would have been to raise just the gold price, but that had not been agreed with Europe. Without the monetary anchors of the Bretton Woods system, inflation became a larger problem from 1973. Oil prices rose as OPEC exerted newfound bargaining power, and food prices spiked due to supply disruption. But the main cause of the 1973 uptick in inflation was arguably the macroeconomic policies in the OECD at the time of loose money which accommodated fiscal deficits. By the end of 1974 gold had reached $195 an ounce, but was volatile, not least due to official gold sales. With inflation, more instability in global (now floating) exchange rates had returned. Countries with large domestic investor bases managed to borrow in their own currencies, which they could then debauch. Without the discipline of the gold standard or fixed parity with the dollar, deficit spending became the norm and inflation became a preferred form of taxation. Gold had been a part of the global monetary system from prehistory to 1971, but has not been since and may never be again. Indeed, in a world of floating exchange rates, inflation may again be used as the principal method of reneging on Western government debt obligations. 1.3.2 ideological shifts After World War II, involving as it did enormous state economic planning and mobilisation by all of the major combatants, the consensus was for the state to continue taking a prominent

The creation of IMF Special Drawing Rights (SDRs) in 1968 was designed to create a new reserve currency and complement the limited stock of gold to meet growing reserve asset demand, but this need was averted by the growth of the Eurodollar market – private sector borrowing in dollars offshore (in Europe) – which reserve managers could purchase instead of US official debt assets. See Isard (2005), p. 33. 26 For a more detailed account see Eichengreen (2011). 25

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Emerging Markets in an Upside Down World

role in the economy. Added to that were the anthropological27 and political priorities of building societies with less inequality, leading to the start of the creation of the modern European welfare state. There were very few economists who thought let alone argued differently.28 With state dominance came government control in a wide range of areas, including (and consistent with the Bretton Woods agreement) European blanket controls on the free flow of cross-border capital.29 The modern period of globalisation is thus associated with the period when these controls came off, which they started to do in 1979 with the lifting of UK exchange controls. The experience of World War II and the need to rebuild afterwards were clearly behind the desire for a large economic role for the state, but ideology was also at play. When this fell to more pro-market ideology in the 1980s, policies shifted dramatically, in some cases with a slapdash disregard for consequences, in the opposite direction. Indeed, I experienced the force of an ideological argument myself. Late in George H.W. Bush’s presidency I was working in the Strategic Planning Office of the US Treasury-controlled Plans and Programs Department of the Inter-American Development Bank. Following a plebiscite in Uruguay against privatisations, a topic which had become highly politicised, there was concern that privatisations might be stopped in several countries. Hence I wrote a technical paper on when and how to privatise (and thus by extension also when not and how not to).30 It was explained to me officially in a bizarre meeting that, while there was nothing wrong with the content of the paper, it was inappropriate that it should be seen to be written from inside the Department. As in Umberto Eco’s Name of the Rose: knowledge is sin. In Eco’s book the medieval library in which the plot is set is eventually burned down to avoid the revelations of knowledge in an ancient Greek text. My paper was banned from distribution, which, perhaps predictably, had the opposite effect to that intended.31 The ‘Washington Consensus’ is shorthand for some of the new thinking. The term was originally coined by John Williamson at a conference in 1989 to describe 10 areas of policy reform being enacted by Latin American governments: prudent fiscal deficit management, public expenditure priorities, tax reform, interest rates, the exchange rate, trade policy, foreign direct investment, privatisation, deregulation and property rights.32 The term was controversial partly because of its name. Frances Stewart has argued, and Williamson did not deny, that by calling it the Washington Consensus as opposed to the more obvious Latin American Consensus, the intention was to create US support for what were highly pragmatic and largely orthodox policies Latin American governments wanted to continue to follow.33 What started as a rough pragmatic list of what was happening soon hardened into (right-wing) dogma. And part of the dogma became free trade and free capital movement across borders: another part of the story of globalisation’s progress. Globalisation was also pushed in the post-World War II era in the successive trade rounds of GATT and then (after 1995) the WTO, though, in consequence of (certainly perceived) For example, the Beveridge report on social insurance and allied services can be seen as anthropological in nature. 28 See Skidelsky (1995). 29 US capital controls only began in the 1960s and had limited effect – though one was to stimulate the creation of the Eurodollar market in 1963. 30 The full paper can be found on Jerome Booth’s blog on newsparta.net. 31 Distribution of an earlier draft escalated. I have been told subsequently the paper was used several years later as a guide for privatising CVRD, now known as Vale, one of the largest companies in Brazil. 32 Williamson, Ed. (1990). 33 Stewart (1997). 27

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23

relative bargaining power at the time, with a clear favouritism to the developed world. The emphasis was on free movement of goods, but also capital and (much later) protection of intellectual property rights. Fligstein (2001) categorises three types of globalisation which followed: an increase in world trade, the rise of the Asian Tigers (Hong Kong, Singapore, South Korea and Taiwan) at the expense of the first world, and the growth in financial markets in debt, equity and foreign exchange. We do not live in a world of free trade though, but in a world of more-or-less free trade, except in agriculture and textiles, where protectionism is alive and well – and highly distorting and disadvantageous to emerging countries.34 Nevertheless, developing countries have benefited enormously from globalisation. They have taken advantage of greater trade opportunities, grown fast and learnt from their own mistakes and those of others. The sharing of policy experiences across countries should not be underestimated as a great stimulus to better policies. While the bottom billion35 in failed states in sub-Saharan Africa and Central Asia remain misgoverned,36 aid-dependent37 and poor, other economies have grown at the highest rates ever recorded in history. Low-skill jobs have moved to developing countries, but higher skilled ones are moving there too. India and China alone (accounting as they do for 36% of world population) have pulled hundreds of millions out of poverty. 1.3.3 Participating in globalisation: living with volatility Protectionist sentiment and the import substitution industrialisation (ISI) ideas of Raúl Prebisch and others to support infant industries, popular in the 1960s and 1970s, went out of fashion with the rise of the Washington Consensus in the 1980s and 1990s. Capital accounts started to be liberalised in the Thatcher/Reagan period in a number of countries, both developed and developing.38 Countries risk incurring severe costs should they cut themselves off from the outside world. Policymakers are right to be concerned about capital flow volatility, but lack of international competition can reduce both productive and allocative efficiency. Nearly all choose to participate in global markets, but some want to retain an element of control. No country, once partially liberalised, is currently planning blanket capital controls, but a number of measures have been tried to reduce capital flow volatility. However, while so-called macroprudential policies (not a clearly defined set of policies, but ones focused on reducing financial sector systemic risks) may be advisable to reduce risks of financial contagion and other spill-over effects from other countries’ financial systems, capital control measures (as opposed to central bank intervention) designed to affect the exchange rate have no clear history of success except in the very short term. Investors seem always, in time, able to devise structures to get around them. Capital controls in the form of taxes on portfolio inflows can moreover be highly distortionary, and once imposed can create uncertainty over future policy and deter inward investment and damage investor sentiment – so raising borrowing costs for the government. In particular, they can discourage long-term institutional investors and encourage the more flighty speculative investors, and this in turn, ironically, can lead to great volatility. Volatility is often a function of fundamental factors inside or outside the country, See for example Booth (1992). Paul Collier (2008). 36 Paul Collier (2010). 37 See Easterly (2006) and also Moyo (2009). 38 See for example Allen (2005), from p. 151. 34 35

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Emerging Markets in an Upside Down World

including rapidly changing perceptions of risk, and heightened by significant offshore pools of capital. We can see globalisation not as the primary cause of contagion or volatility, but rather as the medium. Central bank intervention can reduce volatility, but one needs reserves for that, and the willingness to use them. Beyond that, the way to reduce volatility is to address the fundamental problems, such as fiscal and other domestic imbalances, or factors such as global imbalances or bank deleveraging (a regulatory matter). However, the structure of the international monetary system is in dire need of overhaul and is cause for concern – an issue we shall return to.

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Contents

Acknowledgments Selected List of Acronyms Introduction Chapter 1

xiii xvii xxi

The Islamic Economist/Activist Khurshid Ahmad Early Influences: Muhammad Iqbal, Muhammad Asad, and Sayyid Abul A’la Mawdudi Operationalising Islam: Sayyid Abul A’la Mawdudi and Jama’at-e-Islami Milestone: First International Conference on Islamic Economics International Influence: Islamic Economics as an Academic Discipline Spreading the Message Abroad: Europe and the Islamic Foundation in the United Kingdom Islamisation of the Pakistani Economy Islamic Economics versus the Narrow Pursuit of Profit

1

3 5 8 10 14 15 19

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viii

Chapter 2

Chapter 3

Chapter 4

CONTENTS

Forty Years On: The Wood for the Trees The Last Word

21 24

The Very First Mover Saeed Bin Ahmed Al Lootah Always Begin at the Beginning Dubai: The Environment Dubai Islamic Bank: Early Response First Islamic Banking Conference—May 1979, Dubai Development and Challenges DIB, UAE, and Corporate Governance Hajj Saeed, Dubai, and the Islamic Economy Forty Years On The Last Word

25

The Well of Influence Prince Mohamed Al Faisal Al Saud Germination Catalysis: The Establishment of Dar Al-Maal Al-Islami The Business of DMI: Navigating Uncharted Waters Current Holdings Forty Years On: ‘‘The Aura Is Much Bigger than the Reality’’ ‘‘The Muslim World Went to Sleep’’ ‘‘Eventually, I Think, Everybody Will Become a Salafi’’ The Last Word

47

Steadily Spreading the Blessings Saleh Abdullah Kamel Spreading the Baraka Gone West: Al Baraka in the United Kingdom Advancing the Islamic Economy Islamic Megabank

81

27 30 32 34 35 39 41 43 45

48 52 58 62 64 72 77 80

84 88 91 91

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Contents

World Zakat Fund The Halal Industry Ask Not What the Community of Islamic Countries Can Do for You Forty Years On: Mechanisms over Maqasid The Last Word Chapter 5

The Systematic Rise of a National Industry Mahathir Mohamad and the Malaysian Story Lead-up to the 1981 Decision Influence #1: Tabung Haji Influence #2: Tunku Abdul Rahman, the OIC, and the IDB Influence #3: Prince Mohamed Al Faisal Al Saud

National Steering Committee and Establishing Bank Islam Building an Industry Systematically Setting the Pace and Character of Overall National Economic Development Phase 1: 1983 to 1993—Establishment and Entrenchment Shari’ah-Compliant Financial Instruments Phase II: 1993 to 2000—Liberalisation and Expansion

Chapter 6

ix

93 94 95 96 99 101 102 103 103 105

105 108 108 109 113 114

On Growth and Development Islamic Capital Market: Malaysia as a Global Sukuk Leader A Model Nation for Islamic Finance

116 117 122

Phase III: 2000 and Beyond—Internationalisation

126

The Better System Thirty Years On: And Still Much More to Be Done The Last Word

127

The Islamic Economist Abbas Mirakhor The Second Stage of Islamic Economics Risk Transfer and the Global Financial Crisis

139

132 137

141 145

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x

CONTENTS

Advancing Risk Sharing for the Benefit of All Humanity ‘‘In Islam, the ‘Other’ Doesn’t Exist’’ IMF and Islamic Finance Forty Years On: The Wood for the Trees Building the Roads to a Better Economy The Last Word Chapter 7

Chapter 8

147 151 152 155 158 160

The Global Standard-Setter Rifaat Ahmed Abdel Karim Accounting for a New Paradigm AAOIFI: A Landmark in the History of Modern Islamic Finance Setting the Standards IFSB: Working with the Regulators Changing the Landscape: Integrating Islamic Finance into the Global Financial Architecture Twenty Years of Setting Standards for Islamic Finance Moving Forward The Last Word

161

The Shari’ah Scholar Sheikh Nizam Yaquby The Role of Shari’ah Scholars in Islamic Financial Institutions What Is Shari’ah Compliance? Shari’ah Compliance for an Ethical Society Second-Generation Shari’ah Scholars On Training Shari’ah Scholars for Islamic Finance: Climb the Stairs One by One Two Boards, Three Boards, Four Boards, Five. How Many Is Too Many? Great Strides in Islamic Finance: The Contribution of Shari’ah Scholars

185

‘‘Monumental Fatwa’’: Dow Jones Islamic Market Index

204

Forty Years On: The Wood for the Trees The Last Word

206 208

163 167 168 172 178 181 183 184

188 189 191 196 197 199 203

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Contents

Chapter 9

Chapter 10

Chapter 11

xi

The Lawyer Michael J.T. McMillen Many Firsts ‘‘The United States Is Probably the Second Largest Islamic Finance Market in the World’’ ‘‘The United States Is One of the Easiest Places in the World to Do a Shari’ah-Compliant Deal’’ Consulting and Structuring Deals Worldwide Critical Factors for the Development of Islamic Finance Code of Conduct: Lawyers and Shari’ah Scholars

211

Sheikh Muhammad Taqi Usmani Sukuk Pronouncement On Freely Circulating Fatawa

228 229

101: On Wholesale and Retail Maqasid al Shari’ah and the Non-Muslim Islamic Finance Lawyer Seventeen Years On: The Woods for the Trees The Last Word

230

The Equity Capital Market Man Rushdi Siddiqui Building Indices and Benchmarks for the Global Industry Global Viability: Outperforming Conventional Indices and Averting Enron Wherefore the Pulse?: Shari’ah-Compliant and Shari’ah-Based Indices Gaps and Disconnects Major Disconnect: On Information The United States of America, Islam, and Islamic Finance The Halal Industry The Last Word More than the Sum of Its Parts: Forty Years of Islamic Finance Growth beyond Expectations My Shari’ah, Your Shari’ah: What Is So Authentically Islamic about This System?

213 217 220 222 222 226

232 233 234 237

239 241 244 248 252 253 256 258 261 263 266

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xii

CONTENTS

A Viable Business Model Beyond Banking and Finance: The Islamic Economy The Last Word: Commitment to the Islamic Basis as a Better Way Glossary References About the Author Notes Index

270 273 275 279 283 297 299 315

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Chapter 1

The Islamic Economist/Activist Khurshid Ahmad

Whoever rallies to a good cause shall have a share in its blessings. —Al Qur’an, Sura An Nisa, Verse 85

‘‘W

hat Is Islamic Economics?” was the keynote address delivered by Professor Khurshid Ahmad at the World Assembly of Muslim Youth in Riyadh, Kingdom of Saudi Arabia, in 1973. That conference led to his involvement in organising the watershed First International Conference on Islamic Economics in Mecca in 1976. Professor Khurshid’s vision and activism planted the seeds for the development of Islamic Economics at the institutional, political, and academic level. He started the first ever academic Islamic economics programme in the 1960s at Karachi University, and served as the founding chairman of the International Institute for Islamic economics at the International Islamic University in Islamabad, established in 1983. Nationally he has 1 click to learn more about this book on wiley.com


2

GLOBAL

LEADERS

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been involved with the Islamisation of Pakistan’s economy since 1978. Professor Khurshid’s work also extends to the United Kingdom, where the Islamic Foundation, an institution he founded in 1973, has been a major influence. Khurshid Ahmad was born in 1932 in Delhi, India. It was a time of In my youth I was heightened debates and demonstrations groping in search of an over the position of Indian Muslims ideology and a life as members of a minority community in British India amid the much wider mission. The choice was struggle for Indian self-government. By between socialism, which the time Professor Khurshid was born, had glamour for the Mahatma Gandhi had become the domyouth at that time, or inant figure in Indian politics, after more than a decade of campaigning going back to my own for self-government from the British; roots, Islam. his nonviolent movement became more —Professor Khurshid important after the bloody Amritsar Ahmad massacre of 1919 that saw British and Gurkha troops kill hundreds of peaceful and unarmed demonstrators protesting British forced conscription of Indian soldiers and the heavy war tax. As part of his political activism, Mahatma Ghandi called for a new way of economics, or “true economics,” as he put it. His call was for economics that “stands for social justice, or promotes the good of all equally including the weakest, and is indispensable for decent life.”1 But where Gandhi based this “decent life” on Hindu precepts of dharma (righteousness), artha (material wealth), kaam (happiness), and moksha (freedom from life), Indian Muslims were devoting their energies to establishing Islam as the foundation for their separation from predominantly Hindu India.2 Professor Khurshid grew up in this highly charged nationalist and Islamic revivalist atmosphere. He first received traditional Islamic schooling and then attended Anglo-Arabic Higher Secondary School, where he excelled academically. Stoked by the political overtones of the day, his pedigree and abilities as an activist and leader shone through from a very young age. A year before the partition of India, in 1946,

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he was elected president of the Children’s League in Delhi. In this capacity at the age of 14, he regularly led demonstrations for Pakistan’s independence. Following partition in 1947, his family emigrated, first to Lahore, then to Karachi. In 1949 he wrote his first article, on Pakistan’s budget, which was published in the Muslim Economist.3 In that same year he made the decision which would prove to be the wellspring for his life’s work: he became a member of the Islami Jamiat-e-Talaba, the autonomous students’ organisation that draws its inspiration from the Islamic revivalist party Jama’at-e-Islami founded by Sayyid Abul A’la Mawdudi in 1941. From early on Professor Khurshid looked to Muslim thinkers who were searching for a new interpretation of Islam suited to the modern context. “In my youth I was groping in search of an ideology and a life mission. The choice was between socialism, which had glamour for the youth at that time, or going back to my own roots, Islam.” He further explained his early influences: “It took about two years to discover that Islam should be my destiny. In this search I was influenced by Iqbal, Muhammad Asad and Mawdudi. But the most profound and all-embracing influence was that of Mawlana Mawdudi.”

Early Influences: Muhammad Iqbal, Muhammad Asad, and Sayyid Abul A’la Mawdudi Much of the drive and early impetus for Professor Khurshid’s work was informed by the activism and influence of these three leading Muslim thinkers of the twentieth century. The overwhelming discourse of Islamic revivalists like Sayyid Abul A’la Mawdudi and Muhammad Iqbal was political. All three planted within the Muslim consciousness the notion that Islam provides a holistic solution to human life as faced in the modern era. This was the prevalent contemporary Indian Muslim perspective in the face of the two-pronged challenges: colonial occupation and the predominant Indian Hindu design for an independent India. Muslims in India were seeking to both oust their British colonial masters and establish an Islamic system they could live by. The fourteen-year-old Khurshid Ahmad who led student demonstrations for the Muslims of India was very much influenced by

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Muhammad Iqbal. Iqbal (1876–1938) is far and away the predominant individual to have “captured the minds and imaginations of Muslims in India-Pakistan.”4 In his famous 1930 Allahabad Address, Iqbal advanced the idea for a separate land for the Muslims of India. (While Iqbal has been largely credited for the political vision of the separate nation-state for Indian Muslims, others argue for Chaudhari Rahmat Ali as the first to have coined the term “Pakstan”—a combination of Punjab, Afghan Province (North-West Frontier Province), Kashmir, Sind and Baluchistan—and enunciate a systematic plan for the formation of the Muslim nation-state.5 ) Iqbal described Islam as a “social nizam” (social order) and argued that the religion was the best suited of all religions for the organisation of life, as it applied to every aspect of the life of the individual and society. He also called on the leaders in Europe, especially colonial Great Britain, to take a serious interest in Islam beyond culture and religion and to see it for its economic system that provides practical solutions to contemporary problems.6 Iqbal and Muhammad Asad knew each other well. In his 1954 autobiography The Road to Mecca, Muhammad Asad called Iqbal “the great Muslim poet-philosopher and spiritual father of the Pakistan idea.”7 The two met in Lahore when Muhammad Asad ventured there following his life-changing six-year sojourn in Saudi Arabia, from 1926 to 1932, during which time he converted to Islam. (See Chapter 3 on Prince Mohamed Al Faisal Al Saud for encounter between Muhammad Asad and the prince’s late father King Faisal in Saudi Arabia.) Muhammad Asad (1900–1992) was born Leopold Weiss, an Austrian Jewish convert to Islam famed for his travels and writing and his translation and exegesis of the Qur’an. His view on economics and Islam was that “material prosperity is desirable, though not a goal in itself . . . . Islam does not admit to the existence of a conflict between the moral and the socio-economic requirements of our life.”8 When he first met Iqbal in Lahore, Muhammad Asad was on his way farther east to eastern Turkistan (in Central Asia), China, and Indonesia. He never made it there. Iqbal persuaded him to remain in India “to help elucidate the intellectual premises of the future Islamic state.”9 According to Asad, both men agreed that “this dream represented a way, indeed the only way, to a revival of all the dormant hopes of Islam, the creation of

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a political entity of people bound together not by common descent but by their common adherence to an ideology.”10 Iqbal did not live to see the establishment of Pakistan in 1947. Muhammad Asad did, and upon the creation of the new land, he was called on by the government to organise and direct the Department of Islamic Reconstruction. He wrote that the department “was to elaborate the ideological, Islamic concepts of statehood and community upon which the newly born political organisation might draw.”11 As a result of his contribution to the new state, Pakistani citizenship was conferred on him, and Muhammad Asad served as Pakistan’s foreign minister before resigning as the country’s Minister to the United Nations in 1952.

Operationalising Islam: Sayyid Abul A’la Mawdudi and Jama’at-e-Islami Where Iqbal and Muhammad Asad captured the young Khurshid’s imagination of an independent Muslim state and the need to revive Islam as a complete system of life, it was Sayyid Abul A’la Mawdudi’s practical application of the religion that proved most attractive to Professor Khurshid. The professor explains that it was Mawlana Mawdudi’s work that enabled him to have a clear understanding of Islam, its objectives, principles, norms, and the changes it wants to bring about in human thought and society. Professor Khurshid says, “It was in this context that Mawlana Mawdudi’s ideas on economics had profound impact on my thinking.” Mawlana Mawdudi (1903–1979) has often been said to be against the separation of Muslim Pakistan from India, a point that Professor Khurshid strenuously dispels. Mawdudi, according to Professor Khurshid, was not active in politics during the Pakistan movement, which largely fell between the years 1940 to 1947. During this time Jama’at-e-Islami, which was founded in 1941, worked as an ideological movement, not as a political party. Mawdudi, says Professor Khurshid, supported the Two Nation Theory and was one of the most effective critics of the concept of territorial nationalism expounded by Congress and nationalist Muslims. Mawdudi expounded the concept that Muslims

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are a nation based on faith and not territory, i.e., his vision was panIslamic in nature. Although Mawdudi did not participate in the Pakistan movement (a fact that perhaps contributed to later narratives reporting his alleged opposition to separation), he publicly stated his vote for Pakistan when the plan for Partition was announced in June 1947. Mawlana Mawdudi migrated to Pakistan following partition. He was a friend of Professor Khurshid’s father and was a regular visitor to the family home in Lahore. A prolific writer, Mawdudi produced over 120 books and articles primarily on the contemporary application of Islam. His major contribution lies in his magnus opus, Tafhim al-Qur’an, an exegesis of the Qur’an, in which he presented the Book’s relevance to everyday problems. The Tafhim took him more than 30 years to complete. Mawdudi set up Jama’at-e-Islami in 1941 and devoted his life to articulating how Islamic revivalism could be brought about in political, social, cultural, and economic arenas. He attempted to make the religion more “operational” to mobilise the masses. Mawdudi’s ideas were said to have “profoundly influenced” Sayyid Qutb, a leading member of Egypt’s Al Ikhwan Al Muslimun in the middle of the twentieth century.12 On the Indian subcontinent, Mawdudi has been hailed as the person who established the “economics of Islam” as a separate branch of knowledge.13 However he is far better known for his brand of political Islam as spread through his writings and Jama’at-e-Islami. By its own description, Jama’at-e-Islami is “an ideological party”14 that stands for “complete Islam”15 and whose mission, according to Article 4 of the party’s constitution, is the “establishment of Divine Order or the Islamic way of life”16 “in its entirety, in individual and collective life, and whether it pertains to prayers or fasting, Haj or Zakat, socio-economic or political issues of life.”17 From its ideological roots, Jama’at-e-Islami moved into a more political phase when Mawdudi decided that the party would participate in Pakistan’s 1958 national elections18 but the party did not gain much political success until 1977 with the arrival of General Zia ul-Haq in a military coup that overthrew Prime Minister Zulfikar Ali Bhutto. With Zia initially supportive of Jama’at-e-Islami and the Mawlana himself, there were hopes from the party for a complete Islamisation of Pakistan. When Zia finally held national elections in 1985, Jama’at-e-Islami won

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only 10 of the 68 seats it contested in the National Assembly and 13 of the 102 for various provincial assemblies.19 With Jama’at-e-Islami proving to be less of a force than expected on the national stage, Zia’s support for the party waned. Although Jama’at-e-Islami did not become the force in politics it had hoped to be it nevertheless continued to have an influence in Pakistani politics.20 Outside its rank and file, sympathisers and supporters, the party has many critics both at home and abroad especially for its strict interpretation of Islamic norms. Professor Khurshid became active in Jama’at-e-Islami’s student wing, Islami Jamiat-e-Talaba (the Islamic Student Association) in 1950, during his time at the Government College of Commerce and Economics in Karachi. He was elected head of the Karachi group in 1950 and from 1953 to 1955 served as its national president. He latched on to Mawdudi’s approach of making Islam “more operational,” understanding that this notion needed to be advanced to better serve the changing modern context. Professor Khurshid emphasised that Islam is not confined to piety in ritual but that the discipline of Islamic economics has to be “solid” if it is to be presented as a viable alternative system. To this end, he understood that Islamic economics has to be treated as a social discipline distinct from traditional exposition of the economic teachings of the Qur’an and Sunna of the Prophet Mohamed (pbuh). His own entrenched traditional Islamic education was followed by academic achievements at the undergraduate and postgraduate level; he received a BA in commerce in 1953, followed by an MA in economics in 1955, an LLB in 1958, and an MA in Islamic Studies in 1964. With a strong foundation in both Islamic knowledge and “Western” commerce and economics, Professor Khurshid left Pakistan for the United Kingdom in 1968, heading to the University of Leicester, where he was a research scholar until 1973. He continued to live in Leicester until 1978. Between then and his return to Pakistan in 1978, he was a regular speaker at events organised by the UK’s Federation of Student Islamic Societies, speaking generally about Islam. His activism and speaking engagements took him to many countries, including Algeria, Italy, Libya, Malaysia, the Netherlands, Turkey, the United States, Saudi Arabia, and West Germany.

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Milestone: First International Conference on Islamic Economics By 1973 when Professor Khurshid was invited to give the keynote address at the annual conference of the World Assembly of Muslim Youth in Riyadh, he already had more than a decade’s worth of scholarship and activism in Islam and Islamic economics. He had started teaching economics in 1955 at Urdu College Karachi and then joined Karachi University in 1961, where he taught economics until 1968. While there, he developed a course in Islamic economics within the undergraduate programme. The establishment of a master’s programme followed—a programme he claims was “arguably the first of its kind in any university in the world.” Professor Khurshid formally retired from Karachi University in 1974. “My own contributions, humble they are, were, in the earlier phase, to explain what Islamic economics is and show why a new approach was needed in economics. There was also a need to show that riba [interest] is at the root of many problems, not only in the Muslim world, but globally, and that we have to get rid of it.” The Qur’an makes several references to riba. Chiefly, following the line from the Qur’an found in the second chapter, Al Baqara, verse 275, “God has permitted trade and forbidden interest,” most Muslims take the injunction against riba as the cornerstone for Islamic banking and finance, and so its elimination is seen as necessary. (For details, see Chapter 6 on Abbas Mirakhor.) Professor Khurshid addressed these issues in his 1973 keynote address. He also put forward the need for a research centre and a journal for Islamic economics in order to advance the field systematically. These projects were taken on by King Abdulaziz University of Saudi Arabia. (See Chapter 4 on Saleh Abdullah Kamel.) Professor Khurshid served on the Supreme Advisory Council of the research centre from 1979 to 1983. Significantly, his suggestion for an international conference on Islamic economics was taken up. He remembers it well: “Immediately after the conference a small meeting was organised by Dr Muhammad Omar Zubair [president of King Abdulaziz University] and a follow-up meeting was held the very next week. A steering committee was formed to organise the First International Conference on Islamic Economics.” Professor Khurshid was a member of that steering committee.

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The conference was originally scheduled for the end of 1975 but was pushed to February 1976, due to the assassination of King Faisal of Saudi Arabia in March 1975. These initiatives were consolidated and implemented with haste due to the palpable momentum for Islamic solidarity surging through the Muslim world. This Islamic resurgence had started heating up during colonial times and was pronounced in the Indian subcontinent, with the activism and influence of leaders like Muhammad Iqbal, Muhammad Asad, and Mawlana Mawdudi spurring on young Muslims like Professor Khurshid to demonstrate and protest for separate Islamic nation-statehood. Other major Muslim countries were also stirring after independence. Egypt finally saw the British leave in 1952, the French left Morocco and Tunisia in 1956 and Algeria in 1962. In Southeast Asia, Tunku Abdul Rahman succeeded in gaining independence for Malaysia from the British in 1957. In the Arabian and Persian Gulf, the United Arab Emirates was formed in 1971 following the expiry of the British-Trucial Sheikhdoms agreement, and Bahrain and Qatar also became fully independent. In terms of the development of Islamic economics, banking, and finance, the 1950s and 1960s saw early experiments in interest-free models. Most prominent was the Mit Ghamr Savings Bank in Egypt in the years 1963 to 1967 and Tabung Haji in Malaysia from 1963. Otherwise, the interest-based system introduced and practised by their colonial masters was common in the Muslim world, which also adopted European systems of law. Overall, newfound independence increased the tempo and clamour for pan-Islamic solidarity. When Al Aqsa Mosque in Jerusalem, Islam’s third holiest site after Mecca and Medina, fell victim to an arson attack in August 1969, Muslim sensitivities (and rage) were ignited, and leaders established the Organisation of the Islamic Conference just a couple of months later. The movement, which started with 25 Islamic states, was championed and led by the late King Faisal of Saudi Arabia. It is now made up of 57 members. (See the reference to Tunku Abdul Rahman in Chapter 5 on Mahathir Mohamad and the Malaysian story.) The assassination of King Faisal in March 1975 punctuated that Islamic revivalist energy but did not derail plans already set in motion before his death for the establishment of the Islamic Development Bank in 1975 and the organisation of the conference. It was also in 1975

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that Dubai Islamic Bank, the first commercial Islamic bank driven by the private sector, was born. King Faisal’s son, Prince Mohamed Al Faisal Al Saud, went on to honour his father’s wishes to see Islamic banks spread across the world, beginning with the Faisal Islamic Bank of Sudan in 1978 and the Faisal Islamic Bank of Egypt in 1979. (See Chapter 3 on Prince Mohamed Al Faisal Al Saud.) Professor Khurshid considers the 1970s significant years for panIslamic solidarity, with more light being shone on Islamic economics, banking, and finance. Speaking specifically about this, he says, “The [interest-free banking] experiments in Pakistan in the 1950s and in Egypt in the 1960s were stepping-stones. We reached the stage of takeoff in 1975 and 1976 at the operational level.” As to the experiments in the 1950s that Professor Khurshid refers to, there was a short-lived experiment in Pakistan in the late 1950s when rural landlords set up an interest-free credit network.21 The one in Egypt in the 1960s was Dr Ahmad Al Najjar’s Mit Ghamr Savings Bank from 1963 to 1967. Apart from these and Tabung Haji in Malaysia as prominent examples, Muslim countries adopted the conventional interest-based banking model. “I regard the holding of the First International Conference in Mecca in 1976 as the most important milestone in our march towards Islamic economics, banking and finance,” says Professor Khurshid. The conference, whose idea he advocated, is widely viewed as the beginning of the development of Islamic economics and finance as modern scientific disciplines. “What is clear is that Islamic economics is a new paradigm. It’s not just some modification of the capitalistic paradigm. It’s really clear that in Islam, the moral dimension—halal [permissible] and haram [forbidden]—provide a framework in which economic decisions have to take place.”

International Influence: Islamic Economics as an Academic Discipline Building Islamic economics into an academic discipline, or as Professor Khurshid calls it, a “social discipline” would, in the strict sense of “social science,” entail adherence to the scientific method, as science

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must necessarily be value-free. The challenge for Islamic economics is that its basis is not scientific per se, but its fundamental precepts are based on the knowledge as revealed in the Qur’an and the life and teachings of the Prophet Mohamed (pbuh). This is still, in the twentyfirst century, a challenge for Islamic economics. Perhaps unsurprisingly Islamic banking and finance has received the greatest share of attention by Islamic economists, as the banking and finance sectors have largely been driven by business needs and demands. Defining “Islamic economics,” let alone its methodology, is a challenge. In view of the nascent stage and evolving process of the discipline (and Professor Khurshid still considers it nascent and evolving in 2013), a good part of the earlier work on Islamic economics was focused on explaining the fundamental economic doctrines of Islam or attempting to develop viable models for the operation of economic institutions in concert with Islamic teachings. The early works in the 1970s and early 1980s were followed by more scientific and quantitative methods that have more successfully caught the attention of Western economists. (See Chapter 6 on Abbas Mirakhor.) Islamic economists must be well acquainted with shari’ah (Islamic law) and conventional economic theory and methodology. Some of Professor Khurshid’s contemporaries include Anas Zarqa, Mohammad Abdul Mannan, Mustafa Zarqa, M. Akram Khan, Nejatullah Siddiqi, Syed Nawab Haider Naqvi and Umer Chapra. Professor Khurshid may not have produced as much work within the field as his contemporaries, but his activism has truly advanced the field of Islamic economics as an academic discipline. Dr Umer Chapra, who is still actively involved in the field with the Islamic Research and Training Institute (IRTI), an organisation of the Islamic Development Bank, wrote in 1996: However valuable the contributions made by some scholars in their individual capacities, they could not provide the thrust needed to establish the separate identity of the subject. It was the First International Conference on Islamic Economics held at Makkah in February 1976 which served as a catalyst at an international level and led to an exponential growth of literature on the subject. Dr Muhammad Omar Zubair and Professor Khurshid Ahmad played a pioneering role in the

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holding of this Conference as well as a number of other conferences and seminars that have helped provide momentum to the discipline.22 Muhammad Nejatullah Siddiqi’s Muslim Economic Thinking: A Survey of Contemporary Literature, published in 1981 by the Islamic Foundation, cited some 700 Arabic, Urdu, and English titles of original and commentary works on Islamic economics, most written in the decades from the early 1950s to the late 1970s. One matter that the survey brought to light is that the study of Islamic economics had, in the three decades surveyed, been referred to by various names and terms: “Islamic approach” was one, “from an Islamic perspective” and “Islamic economic system” were others.23 Only in the 1970s did the term “Islamic economics” start gaining currency. Following the First International Conference in 1976, Islamic economics entered into the conscience and discourse of international Islamic and Muslim initiatives. The push for the field to be taken seriously as an academic discipline was further supported by its recognition at separate initiatives towards the end of the 1970s and the start of the 1980s. These include the World Conference on Islamic Education and the Universal Islamic Declaration of Human Rights. The resolutions of the First, Second, and Third World Conference on Islamic Education in 1977, 1980, and 1981 studied the needs, aims, and possible curricula for Islamic education at all formal levels. The second conference, held in Islamabad, agreed that an Islamic civilisation course should cover economics and be taught at the university level in all Islamic countries.24 In the same year, the Universal Islamic Declaration of Human Rights, prepared on the initiative of the Islamic Council of Europe which was established in 1973, was penned by scholars, Muslim jurists and representatives of various Islamic movements. It included the objectives and features of an Islamic economic system, stating that it is based on social justice, equity, moderation, and balanced relationships.25 These initiatives contributed to the development of Islamic education, Islamic economics included, in several Muslim countries around the world, including in Pakistan with the International Islamic University in Islamabad (IIUI). The IIUI, established in 1982, grew out of

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the shari’ah faculty at the Quaid-i-Azam University. A year after the IIUI’s founding, Professor Khurshid became the founding chairman of the newly created International Institute for Islamic Economics there, a position he held until 1987. Ziauddin Ahmad, former deputy governor of the State Bank of Pakistan, was its first director general. The institute was a pioneering initiative. To this day it offers undergraduate and postgraduate degrees that are a blend of conventional economics, Islamic economics and jurisprudence related to Islamic economics. It has been a fertile ground for scores of Islamic economists who would go on to contribute to Islamic economics, banking, and finance at the international level. Professor Khurshid remembers the earliest members of the institute: “Munawar Iqbal and Mohammad Fahim Khan were among the senior staff members of the Institute. Munawar Iqbal was a senior research fellow in the Pakistan Institute of Development Economics in Islamabad . . . . It was at my request that he joined the International Institute of Islamic Economics. Both he and Fahim Khan have been the architects of the Institute and later served as its directors general.” Professor Khurshid also remembers the institute’s earliest students: “Tariqullah Khan and Humayon Dar were both students at the institute when I was its chairman. They were among the brightest of our students and have made their mark in the profession.” Dr Tariquallah Khan went on to serve at the IRTI for many years and is still active on the global scene, teaching and speaking about Islamic economics, banking and finance. Dr Humayon Dar is also active on the global Islamic finance scene. From that first institute in Pakistan, other courses began to pop up around the world. Islamic economics, banking and finance began to be introduced in universities in Muslim countries like Egypt, Jordan, Malaysia, and Saudi Arabia. They also began in the United Kingdom. Dr Humayon Dar went on to cofound, with Dr John Presley, Loughborough University’s Master of Science (MSc) programme in Islamic economics, banking and finance in 2000. In the late 1990s, Durham University started offering postgraduate programmes in Islamic finance, driven by Professor Rodney Wilson at the helm. Unlike Loughborough’s programme, which was discontinued in 2005,

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Durham’s has grown from Professor Wilson’s one-man department to the establishment of the Durham Centre for Islamic Economics and Finance, offering Masters and PhD programmes. In 2000, Professor Khurshid’s Islamic Foundation opened the Markfield Institute of Higher Education to offer postgraduate courses in Islamic Studies and Islamic banking, finance and management. Professor Khurshid was its rector from 2000 to 2009. The institute is now an associate college of the University of Gloucestershire, and Professor Khurshid is the chairman of the institute’s board of directors. He is also still a member of the board of trustees of the IIUI.

Spreading the Message Abroad: Europe and the Islamic Foundation in the United Kingdom Professor Khurshid has long harboured the desire to spread the message of Islam and Islamic economics beyond the Muslim world. His activism as a scholar touring Europe from the late 1960s laid the foundation for his interest in establishing a base on the continent. He was most familiar with the United Kingdom, having been based at the University of Leicester from the late 1960s to the 1970s. Very early on in his career, even before the first seminal conference in Mecca in 1976, Professor Khurshid established the Islamic Foundation in 1973. The Islamic Foundation is based in Leicester in the English East Midlands, and specialises in research, education, and publication, working to advance Islamic economics and Islam in general. Leveraging on its position in Europe, it also focuses on interfaith relations. This is an area that Professor Khurshid himself has long been involved in. Since the late 1960s when he was traveling throughout Europe as a scholar and activist, Professor Khurshid became involved in several interfaith dialogues. From 1974 to 1978 he was the vice president of the Standing Conference on Jews, Christians, and Muslims in Europe (a project which celebrated its 40th conference in March 2013), and in 1976 he was the co-chairman of the Christian-Muslim Dialogue in Chembasey, Switzerland. Professor Khurshid still participates in interfaith dialogue events, and the Islamic Foundation publishes the biannual journal Encounters: Journal of Inter-cultural Perspectives.

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In the field of Islamic economics, the Islamic Foundation has served as a research centre and publisher of books and journals as well as a centre of education and training through its Markfield Institute. Its Islamic Economics Unit opened its doors in 1976 and since then has produced some 30 titles, in addition to its biannual Review of Islamic Economics. Muhammad Nejatullah Siddiqi’s Muslim Economic Thinking: A Survey of Contemporary Literature was one of the first of its publications, followed by Insurance in an Islamic Economy by the same author in 1985. The Islamic Foundation has also compiled, edited and published much of Mawlana Mawdudi’s work, making it widely available throughout the Muslim world. Two important titles are Let Us Be Muslims (1985), a book that Khurshid himself has been greatly influenced by, and First Principles of Islamic Economics (2011), which was translated from Urdu by Shafaq Hashemi, a senior fellow of the Institute of Policy Studies in Islamabad, and edited by Professor Khurshid.

Islamisation of the Pakistani Economy In 1978 the newly installed military leader, Zia ul-Haq, announced the Pakistani government would enforce Nizam-i-Mustafa, or the Islamic system. Professor Khurshid was summoned back from the United Kingdom, and he assumed the position of deputy chairman of the Planning Commission. He was appointed to the position, he said, as an economist to overlook the Islamisation of the country’s economy. Professor Khurshid went on to become chairman of the Standing Committee on Finance, Economic Affairs, and Planning during his first tenure in the Pakistani Senate from 1985 to 1997. However, as he points out, Pakistan’s vision of Islamic economics was embedded 30 years earlier in the Objectives Resolution passed by the Constituent Assembly of Pakistan in 1949, following independence, but very little effort was made to actualise that vision. He wrote, “It was during General Zia ul-Haq’s period that some piecemeal and disjointed efforts were made in that direction. That process too did not go far enough. Most of the changes were only ornamental. That is why they could not bring any substantial change in the system.”26 The Pakistani government was keen on completely Islamising the country’s economy, but this did not go as planned. They realised,

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in 1991, that the intended switch to the preferred profit and losssharing system as a replacement for the interest-based system had not, in fact, taken place. Professor Khurshid considers these failed attempts largely the result of a lack of political will. The main drive to Islamise the economy was based on the elimination of riba, or interest, since the Qur’anic injunction against riba is vehement. The ideal alternative to an interest-based system is profit and loss sharing, which still remains under-used in the global Islamic finance industry today due to its higher risks. Instead, the Islamic finance industry is considered largely risk-averse and primarily uses debt-based modes such as murabaha (cost plus profit) and tawarruq (a commonly used method designed to provide liquid assets such as working capital finance and short-term financing), both of which either resemble conventional interest-based products or have very little links to the real economy, which is an important requirement of shari’ah. As can be seen in Table 1.1, it was the government that first started to falter on the plan, and to this day the Pakistani economy is still struggling to achieve its aim of being fully Islamised. National efforts continue and in July 2013 there was a flurry of government announcements advancing Islamic finance. The State Bank of Pakistan, the country’s central bank, announced its intention to further promote Islamic banking with a national mass media campaign. As at March 2013, Islamic banks in Pakistan held approximately $8.44 billion, or 8.7 percent of total banking assets. The government is aiming for Islamic banking share of the country’s total banking sector to reach 15 percent by 2017.27 Also announced in July 2013 was the adoption by the central bank of a global standard for sukuk (commonly referred to as Islamic bonds), a move designed to attract foreign inflows especially from shari’ah-sensitive investors from the Gulf Cooperation Council (GCC) countries as well as Southeast Asia and elsewhere.28 As can be seen in Table 1.1, in 1999 the Sharia Appellate bench of the Supreme Court instructed the government to eliminate all forms of interest-based banking by 2001. Professor Khurshid was one of the witnesses to the 1999 Supreme Court decision and considers the judgment “historic.”

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The Islamic Economist/Activist Table 1.1 1980

1981 1984 1985 1991 1992 1993 1997 1999

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Timeline of Islamisation of the Pakistani Economy Council of Islamic Ideology report on the elimination of riba from the economy produced by two committees of experts on the panel of Bankers and Economists under the leadership of Ehsan Rashid, then vice chancellor of Karachi University, and Ziauddin Ahmad, then deputy governor of the State Bank of Pakistan. The proposed strategy was to begin with ruling out riba starting with the government sector and then moving on to eliminating riba from the whole economy. The government did not follow the proposed strategy and instead first reformed three financial institutions—the National Investment Trust, Investment Corporation of Pakistan and House Building Finance Corporation. Profit and loss sharing accounts of commercial banks introduced, effective January 1, 1981. State Bank of Pakistan introduced 12 tools of Islamic finance. Commercial banks claimed to have switched over to profit and loss banking. Federal Shariat Court judged that the switchover to profit and loss banking had not taken place. Federal Shariat Court declared all forms of interest-based banking un-Islamic. Government put in place the Shariat Enforcement Act. However, the Islamisation Commission’s interim report was never presented to Parliament and plans were never implemented. Islamisation Commission reconstituted in 1997, drawing largely on the Report of the Self-Reliance Committee, of which Professor Khurshid was the chairman. Shariat Appellate Bench of the Supreme Court, on December 23, 1999, upheld the 1992 judgement and directed the government to eliminate all forms of interest-based banking by June 30, 2001. The verdict consisted of three detailed judgements by Justice Khalilur Rehman, Justice Wajihuddin and Justice Mohammad Taqi Usmani. United Bank Ltd, a state-owned bank, filed a review petition against the ruling in the Supreme Court of Pakistan. The government supported this review petition against the ban.

SOURCE: Information gathered from Khurshid Ahmad, 2004 and 2010.

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“It covered all the dimensions from shari’ah, as well as from the economic, monetary and financial viewpoints. A roadmap was given as to the transformation of the economy. After that things began to change slightly but again because of the lack of political will, the entire report could not be implemented. But somehow the central bank started to move in that direction and that is how, from 2002 onwards, Islamic banks or banks which would have totally self-contained Islamic units came into existence.” Overall, Professor Khurshid laments the historical process of Islamising Pakistan’s economy and reiterates the lack of political will. But he thinks the country is now on the right track. “Now, [progress] is very slow but the direction is correct. But there are a number of problems because of the lack of political will and a lack of supporting ancillary activities that should have taken place in other parts [of the economy] because of the non-implementation of the Supreme Court judgment [of 1999]. We had wanted to create laws, or amend or change laws. Yet now we have two parallel streams, about 90 percent in interest-based and about 10 percent Islamic-based.” At the age of 80, and after 33 years of his country’s unsuccessful attempts at completely Islamising the economy, Professor Khurshid has not given up any hope and still continues to chip away at the predominant interest-based system, a testament to his firm belief, commitment, and dedication to the cause. He continues to work in the UK at his Islamic Foundation and in Pakistan with his Institute of Policy Studies to advance Islamic economics. Professor Khurshid describes a new phase of mobilising the Pakistani masses towards the Islamic system: “We are now again in an exercise to mobilise for a bottom-up approach. That is the grassroots for Islamic finance. Financial products not only help with growth but also in distributing justice, generating employment, and including [economic] participation of people at the lower level, with the small and medium industries. From this we find a new beginning. I hope that with the two parallel movements—top-down from the state level and bottom-up involving and inculcating the private sector, especially the agricultural sector and the cottage industries—we can change things in a more effective manner, and in a shorter period of time.”

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The Islamic Economist/Activist

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Islamic Economics versus the Narrow Pursuit of Profit In 1973 Professor Khurshid Ahmad asked, “What Is Islamic Economics?” in his keynote address to the World Assembly of Muslim Youth in Riyadh. Forty years on he answers, “Islamic economics, to me, is an evolving nascent discipline. It’s no longer just economic teaching of the Qur’an and the Sunna. Now it is a new social discipline.” What is Islamic economics? “My short answer is that Islamic economics consists of the study of economics from an Islamic perspective. It covers all areas of concern to conventional economics, including vision, analysis, policies and history. However, what makes Islamic economics distinct is its value framework and the normative parameters within which the entire study and practice of economics is to take place. Economics, in this framework, becomes instrumental in promoting simultaneously the ends of development, efficiency, equity and well-being for all.” In 2005 Professor Khurshid explained that the Islamic approach to economics addresses not only externalities but encompasses a wider matrix, a more holistic system of betterment and development. He wrote: The Islamic scheme for social change and regeneration of human societies is unique as it is based on a methodology that is different from the one pursued by all major economic and political ideologies of post–Enlightenment Europe and America. The methodology and strategy of this change, as developed and practised in contemporary secular societies, has assumed that a radical transformation of humans can be brought about by changing the environment and society’s institutions. That is why emphasis has always been placed on external restructuring. The failure of this method lies in ignoring individual persons as its real focus—their beliefs, motives, values, and commitments. It ignores the need to bring about change within men and women themselves, and concentrates more on

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change in the outside world. What, however, is needed is a total change—within people themselves as well as in their socioeconomic environment.29 The Islamic framework does not condemn profit-making but does not condone the narrow pursuit of profit. Profit-making lies within a framework of halal and haram. Professor Khurshid added: Self-interest is a natural motivating force in all human life. But self-interest has to be linked to the overall concept of good and justice. Reward for effort and suffering for failure in effort provide the best framework for human society and the economy. Islam acknowledges it and accepts it as a first principle for economic and social effort. But Islam also lays down a moral framework for effort, spelling out values and disvalues, what is desirable and what is reprehensible from a moral, spiritual, and social perspective. Halal (permissible) and haram (forbidden) provide a moral filter for all human actions.30 With the failures of the interest-based system leading to the global financial crisis that started in 2007/8, the furore in the UK over bankers’ bonuses and the London Interbank Offered Rate (LIBOR) scandal that broke in mid-2012, there has been a widespread call for morality and ethics to be re-injected into banking and finance. Not many who have followed the LIBOR fallout would forget former Barclays CEO Bob Diamond being acrimoniously reminded by John Mann, a member of the UK House of Commons Treasury Select Committee, of the three founding principles of the Quakers who set up Barclays: honesty, integrity, and plain dealing.31 Beyond normative professions, modern Islamic economics, banking, and finance over the last 40 years have slowly been constructing an operational framework based on ethics, justice, and morality. Addressing the recent global financial crisis that started in 2007/8, Professor Khurshid wrote that Islamic economics must be paid special attention in light of the need to reform the global capitalist economic system and to revise the fundamentals of the discipline of economics. With the failure of free markets, his view is that morality and ethics must be re-discovered and re-established.32 He is certain that Islamic economics has the answers but concedes: “There’s a long way

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The Islamic Economist/Activist

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to go. Unfortunately the dominant system remains the global capitalistic system. Similarly, local vested interests are much more powerful.” However, the advancements made by Islamic banking and finance over the last 40 years are causes for optimism.

Forty Years On: The Wood for the Trees Professor Khurshid cites four aspects that he considers achievements for Islamic economics, banking, and finance since the 1970s: 1. A well-thought-out critique of an interest-based economy in particular, and of conventional economics in general. 2. A clear exposition of the main features of an interest-free economy, Islamic banking and major parameters of an Islamic economy. 3. The introduction of Islamic economics, banking, and finance as a taught subject in academic institutions in different parts of the world and the establishment of a number of research institutes engaged in theoretical research as well as evaluation of operational experiments. 4. The establishment of Islamic banking and financial institutions, which have been increasing over the last 30 years, on an average growth rate of 10 to 15 percent per year. These cater to the financial needs of people who want to undertake their economic and financial activities within an interest-free framework. While Professor Khurshid considers this an evolutionary process, he believes that significant progress has been made. Professor Khurshid Ahmad’s work in the advancement of Islamic economics is a significant thread that runs through the fabric of the Islamic system. However, he considers his contributions and the 40 years of growth and development as mere “first few steps.” He acknowledges some of the landmarks that have pushed the industry forward: “In the field of finance, in the field of banking, investment, takaful, the question of accounting and auditing, I think, in all these areas, noticeable developments have taken place. But it’s still very early.” Does he see a model Islamic economy anywhere in the world? “I don’t see a model Islamic economy anywhere in the world. In fact,

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my view is that first of all you’d have to have a phase of Islamising the economy and then only will we be able to reach an Islamic economy. But definitely progress has been made and I’m quite hopeful about Pakistan, about Iran, about Sudan, about Turkey, about Egypt. I hope that things will move.” He looks around again and says, “Malaysia has also made good progress. Even in Indonesia, which came [into the Islamic system] very late, but the last ten years, I think, have moved quickly in certain areas. But all these, in my view, are only initial, elementary positive developments, and they only bear a very limited influence.” Many challenges still lie in the way of a more complete Islamic economic system. He explains, “First, [there is a] lack of political will within leaderships who wield power. Secondly, [there is a] lack of education and proper understanding [of the subject and system] by the people. Emotionally, they are committed to Islam, but intellectually and operationally, they are not. Thirdly the influence of the conventional global system—while we want to remain fully engaged with it, we want a space for ourselves. That is why we have to set our eyes on attaining self-reliance, so that we could preserve our identity and pursue our priorities. Of course this would take place in the context of our being a part of the global system.” Professor Khurshid completed his second tenure as a member of the Pakistani Senate, serving from 2003 to 2012 (his first was from 1985 to 1997). Combined with his activism and involvement in the socioeconomic sphere, Professor Khurshid has a great deal of experience and intimate familiarity with Pakistan, a country notorious for its widespread corruption. The Corruptions Perceptions Index published by Transparency International since 1995 ranks countries and territories according to their levels of public sector corruption, with 10 being least corrupt. On this index, Pakistan has averaged a score of 2.34 for the 11 years since 2001. The Scandinavian countries, New Zealand and Singapore continue to dominate top rankings with scores between 9.0 and 9.9. Professor Khurshid says that he has tried to impact policy making in his 21 years as a Senate member and in his activism through his Institute of Policy Studies. His work in educating generations of Islamic economists has also slowly moved the ground. But at the end of the

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The Islamic Economist/Activist

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day, more action based on shared vision is needed to tackle multidimensional challenges. “We need political leadership, economic leadership, intellectual leadership that shares a vision and that is committed to it, to mobilise the resources of the state and society. Unfortunately, leadership in most Muslim countries has failed to deliver—whether they are monarchies, dictatorships or so-called democracies.” This is regrettable, to say the least, considering the fervour and cooperation of the Muslims in building pan-Islamic solidarity since the 1960s. As to what he regrets, Professor Khurshid responds: “The list could be long but I will only mention two—first while the Muslim world is resource-rich and Islam has given us a clear vision of a socio-economic order based on our values, the leadership in most of these countries lack political will as well as moral and professional credibility. Because of that we have not been able to move in the right direction. Whatever effort has been made is too slow. Secondly, I feel that experiments in the field of Islamic banking and finance have so far been concentrated more on ‘shari’ah-compliant’ techniques and we have not moved adequately towards what is desired, i.e., ‘shari’ah-based’ system. Similarly so far our approach has been what can be described as a ‘top-down’ approach. It is high time we move simultaneously and more vigorously towards a ‘bottom-up’ approach, involving people and institutions at the grassroots. Both these approaches are inseparable and must go hand in hand. That is my vision for the future.” (See Chapter 10 on Rushdi Siddiqui for discussion on shari’ah-compliant versus shari’ah-based.) Hence, according to Professor Khurshid, there are three main issues: 1. Governments in Islamic countries are incompetent and have not furthered more concerted efforts to implement what Islam offers in the way of an economic system to better use and distribute the abundance of resources found in Muslim lands. 2. Islamic banking and finance has concentrated more on the technical aspects of making products and instruments to circumvent interest as opposed to fulfilling the substance of shari’ah, which is aimed at justice and equity. 3. Any government efforts must be supported by grassroots initiatives. These issues dominate the timeline of the development of Islamic banking and finance, and are not restricted to Pakistan by any means,

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although they affect different countries on different levels and at varying intensities. They are mentioned throughout this book and concern leaders like Prince Mohamed Al Faisal Al Saud, Saleh Abdullah Kamel, Abbas Mirakhor, Rushdi Siddiqui, and the leaders and experts in Malaysia’s Islamic finance industry. It is regrettable for the bigger vision of the Islamic economy that the fervour for Islamic solidarity petered out following the establishment of the first institutions, such as the Organisation of the Islamic Conference in 1969 and the Islamic Development Bank in 1975. It is perhaps meaningless to wade into counterfactuals, but who knows what would have happened if the global Muslim community had followed through with its momentum towards a true pan-Islamic solidarity to advancing Islamic economics, banking, and finance.

The Last Word Professor Khurshid is optimistic about the next 40 years, saying in no uncertain terms: “I am confident in the next 40 years the development of Islamic economics, banking and finance will take place in all the major areas of theory and practice. This would result in efforts towards transforming the entire financial and economic system on the normative foundations given by Islam. Initially this movement will gain strength in the Muslim world, and then hopefully this may become a model to influence the rest of the world. However, all this would depend on the clarity of our vision and sustained loyalty to it, as well as on the persistent professional integrity, high degree of efficient organisation, good governance and, most importantly, effective political will.”

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Better Banking

Understanding and Addressing the Failures in Risk Management, Governance and Regulation

ADRIAN DOCHERTY FRANCK VIORT click to learn more about this book on wiley.com


Contents Acknowledgements

xi

1 Introduction 1.1 Overview and Objectives 1.2 Quick Start Guide to Banking Concepts and Regulation

1 1 4

2 The Global Financial Crisis 2.1 From Deregulation to dotcom Crash 2.2 The Seeds of a Crisis 2.3 “Why Didn’t Anyone See This Coming?” 2.4 The Beginnings of a Crisis 2.5 The Crisis Intensifies 2.6 Meltdown: The Lehman Bankruptcy 2.7 Massive Intervention Internationally 2.8 Sovereign Crises 2.9 Aftershocks and Skeletons in the Cupboard 2.10 Who is to Blame?

7 7 9 14 17 20 21 23 29 32 34

3 Methodologies and Foundations 3.1 How do Banks Make or Lose Money? 3.2 What’s a Bank Worth? Key Issues in Accounting for Banks 3.3 What is Risk? 3.4 What is an RWA? 3.5 What is Capital? 3.5.1 Regulatory Capital 3.5.2 Hybrid Capital 3.5.3 Economic Capital and Ratings Capital 3.5.4 Cost-of-Capital and Return-on-Capital 3.5.5 Capital in a Stress Scenario 3.5.6 Bail-in Capital

37 37 41 47 55 61 63 65 66 66 67 67

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viii

Contents

3.6 What are Liquidity and Funding? 3.6.1 Concepts 3.6.2 Liquidity Management and Liquidity Risk 3.6.3 Interbank Funding, the Money Markets and Central Bank Support 3.6.4 Deposit Guarantee Schemes 3.6.5 Securitisation 3.6.6 Covered Bonds 3.6.7 Liquidity Stress Tests 3.7 What is a Derivative? 3.8 Mark-to-Market and Procyclicality 3.9 Role of Regulation, Supervision and Support 3.10 Ratings Agencies and Credit Ratings 3.10.1 Moody’s Bank Methodology 3.10.2 S&P’s Bank Methodology 3.10.3 Structured Finance Ratings 3.10.4 Use of Ratings in Regulation and Investment Policy 3.11 Analysts, Investors and Financial Communication 4 Regulation of the Banking Industry 4.1 The Relevance of Bank Regulation and Supervision 4.2 Regulation and Supervision of the Banking Industry Prior to “Basel I” 4.3 The Basel Capital Accord aka “Basel I” 4.3.1 Definition of Capital 4.3.2 Deductions Regime 4.3.3 Risk-Weighting Approach 4.3.4 Ratio of Capital to Risk Weighted Assets 4.3.5 Modifications to Basel I 4.3.6 Impact of Basel I 4.4 Basel II 4.4.1 Objectives of Basel II 4.4.2 The Three-Pillar Approach 4.4.3 Pillar 1: Minimum Capital Requirements 4.4.4 Pillar 2: Supervisory Review 4.4.5 Pillar 3: Market Discipline 4.4.6 Capital Calibration 4.4.7 Capital Supply and Mix 4.4.8 Implementation of Basel II 4.4.9 Critique of Basel II 4.5 Basel III 4.5.1 Definition of Capital 4.5.2 Deductions 4.5.3 Risk-Weighted Assets 4.5.4 Minimum Capital Levels 4.5.5 Leverage Ratio 4.5.6 Liquidity and Funding 4.5.7 Derivatives Risk Management 4.5.8 Implementation and Transition

68 68 69 72 76 77 81 83 84 90 96 101 104 104 105 107 109 113 113 113 117 118 119 120 121 121 122 122 123 123 124 130 132 133 135 136 137 141 142 143 145 147 150 152 155 156

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Contents

ix

4.5.9 National Versions of Basel III 4.5.10 Major Achievements of Basel III: Top Five 4.5.11 Major Issues with Basel III: Top Five 4.6 Resolution Regimes 4.7 Other Current Regulatory Workstreams

157 158 161 163 171

5 Case Studies 5.1 RBS 5.2 Dexia 5.3 HBOS 5.4 HSBC 5.5 Bear Stearns 5.6 Merrill Lynch 5.7 AIG 5.8 JP Morgan 5.9 Barclays 5.10 UBS 5.11 Northern Rock 5.12 Bankia-BFA 5.13 Australia 5.14 Canada 5.15 Summary of “Lessons Learned” from the Case Studies

175 176 179 184 189 192 196 200 207 213 217 221 223 228 233 236

6 Objectives and Design Principles 6.1 Free Market versus State Capitalism 6.2 Are There Alternatives to Banks? 6.3 Benefits and Limitations of Finance 6.4 How Much Risk Can We Tolerate? 6.5 The Role of Regulation and Supervision 6.6 The Role of “the Market” 6.7 Conclusions: Proposed Objectives and Design Criteria

239 240 242 250 251 254 256 258

7 A Blueprint for “Basel IV”

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7.1 Risk Management 7.1.1 Valuation Approaches 7.1.2 Role of Internal Risk Assessments 7.1.3 Putting Risk Management at the Heart of Banking 7.1.4 Role of Risk Benchmarks 7.1.5 Assets Don’t Have Risk, Institutions Do 7.1.6 Risk Disclosure 7.2 The Guardian Angel 7.2.1 Key Aspects of the Guardian Angel Model 7.2.2 Creating the Supervisory Elite 7.2.3 Paying the Bill 7.2.4 Cross-Border Issues 7.2.5 The Role of Macroprudential Regulation

261 261 263 263 266 266 267 269 271 273 273 274 275

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x

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7.3 Human Capital 7.3.1 Competence and Incompetence in the Banking Industry 7.3.2 Compensation 7.3.3 Culture 7.4 Governance 7.4.1 How Does Governance Work in Theory? 7.4.2 Improving Governance of Banks 7.4.3 The Centurion Approach 7.5 Capital 7.5.1 The Role of “Capital” 7.5.2 Principles-Based Solvency Regulations 7.5.3 Need for Capital 7.5.4 Supply of Capital 7.5.5 Role of “Hybrid” Capital 7.5.6 Life without Capital Ratios 7.5.7 Minimum Capital Standards 7.5.8 Disclosure of Capital Assessment 7.5.9 Resolution of Failed Banks 7.6 Liquidity and Funding 7.6.1 Liquidity 7.6.2 Funding 7.7 The “Pillar 2” Mindset 7.7.1 Putting Scenario Assessment at the Heart of the Risk Management Process 7.7.2 Wargaming Instead of Stress Testing 7.7.3 Communication of Pillar 2 Findings 7.7.4 The Modular Approach to Risk Assessment 7.7.5 Risk Appetite and Reverse Stress Testing 7.8 Glasnost: Market Discipline and “Pillar 3” 7.9 Industry Structure 7.9.1 Different Types of Banks 7.9.2 Too Big to Fail 7.9.3 Cross-Border Issues 7.9.4 Ownership Structures

275 276 279 285 287 287 289 290 291 292 292 293 293 294 296 297 298 298 299 299 301 303 304 304 308 309 310 311 313 313 319 321 322

8 Challenges 8.1 Industry Entrenchment 8.2 Human Behaviour 8.3 Performance Management for Good Risk Management 8.4 Timing 8.5 A Positive Challenge: Technological and Social Progress

327 327 329 332 333 333

9 What Next? A Call to Arms

335

Glossary & Jargon Lookup

339

Disclaimer Regarding Excerpts from S&P Materials

349

Index

351

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1 Introduction 1.1 OVERVIEW AND OBJECTIVES If the terrain and the map do not agree, follow the terrain.1 Today, the world is in its sixth year of a deep and damaging financial crisis. The cause of this crisis was a massive failure of risk management and governance: quite simply, we lost control of our financial system. As a result, we experienced a debt-fuelled boom that turned rapidly into an economic “bust�. Millions are suffering as a consequence: for example, youth unemployment has risen in the last ten years from 17.8% to 22.8% in the EU and from 12.0% to 16.2% in the United States.2 The problems could have been greater. The bankruptcy of Lehman Brothers could have led our modern, global economy to freeze. Such problems have been averted by the pumping of huge amounts of extra money into the financial system by central banks at low interest rates. These monetary policies are sure to have painful side-effects in the long term, but they have succeeded in keeping our economies moving and bought time to fix the causes of this financial crisis. Banks are lead actors in the crisis. In many countries, large swathes of the banking industry failed and had to be supported by the state. In general, banks had been loosely supervised and some had been badly managed. Seeking ever-increasing profits, the banking industry took huge risks that were not apparent at the time but that we can now see were unacceptable. Problems emerged first in the US subprime mortgage market, which enabled poor people to buy expensive homes. New financial products used financial alchemy to turn this high-risk lending activity into seemingly low-risk investments for gullible investors. They were anything but low-risk: one study of $640bn worth of securities shows that investors lost two-thirds of their money.3 Many of the most gullible investors were banks themselves, often banks outside the USA. The subprime malaise of over-confidence followed by ruinous losses spilled over into other markets and other countries. Common sense should have told us from the outset that this kind of alchemy was impossible and that someone stood to lose out. In the end, it was society that was bearing those risks unwittingly. When the banks failed, society was forced to stump up the financial resources to prop up the system or face chaos and oblivion. The public is rightfully angry about the burden of those losses, but also with the odious behaviours in the banking industry that have been uncovered by the financial crisis: greed, incompetence, negligence, arrogance, contempt, deceitfulness. Several of the leaders of the

Attributed widely to a Swedish Army training manual. Labour Force Survey: Unemployment rate for age group under 25 years 2002–12, Eurostat. 3 Collateral Damage: Sizing and Assessing the Subprime CDO Crisis, Federal Reserve Bank of Philadelphia, May 2012. 1 2

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2

Better Banking

banking industry, who had been lauded as superheroes and feted with honours and multimillion dollar bonuses during boom years, turned out to be incompetent or even downright villainous. No-one disagrees that change is needed, in order to learn the lessons of the current financial crisis and enable us to reduce the likelihood, frequency and impact of future crises. There is a risk, however, that the diagnostic is incomplete and the remedial actions may be ineffective. This book aims to contribute to an improved understanding of the diagnostic as well as offering some additional and alternative proposals for consideration. Current diagnoses tend to focus on the symptoms of the current financial crisis (e.g. the banks’ excessive leverage, weak capital bases, poor funding profiles and insufficient liquidity buffers4) or play the blame game, singling out scapegoats in order to make the resolution of the problem punchier and more streamlined. Requiring higher levels of capital and exposing bad behaviour by bankers should solve the matter, apparently, all at little cost to the rest of us. Such views are incomplete. A better diagnostic should do two things. Firstly, it should recognise the contribution of global macro-economic imbalances – especially the growing indebtedness of western consumer economies – to the current financial crisis. These imbalances are as much political as they are financial. They are also stubbornly difficult to reduce. Secondly, the diagnostic of the banking industry’s problems should centre squarely on failings of governance, regulation and risk management. Society failed to control adequately the banks and the banks failed to manage adequately the risks they were running. Problems of excessively aggressive financial profiles, bad behaviours and excessive pay are consequent symptoms of the failure of governance, regulation and risk management. Society – the ultimate owner of the banking industry – must accept its responsibility for heaping praise on the “banker’s new clothes”, to extend a recently used metaphor.5 In order to advance this diagnostic, there is a need to engage a broad audience. A discussion that is restricted to dedicated professionals from the banking industry and the authorities may miss the broader picture and get lost in cul-de-sacs. Certain arcane elements of the regulatory response to the financial crisis (known as Basel III and covered in Section 4.5) indicate that this is the case. “Expert” diagnostics may also fail to achieve acceptance from the public, who are, after all, the “society” that ultimately carries the can. In the spirit of active engagement, therefore, we seek to set out a basic understanding of the nature and fundamentals of banking, to act as a methodological backdrop to the discussion and assist a simultaneous broadening and simplification of the subject. For example, a basic common understanding of the notions of risk and capital will help any diagnostic on the solvency and resilience of our banks. An elegant diagnostic and a critique of the current regulatory response would be a noble objective for this book, but it would not be sufficient. Therefore, we have tried to set out some concrete, high-level, novel proposals for “better banking”. All of these are to do with bank governance, regulation and risk management. To begin this task, we have had to assume at the outset that politically, a liberal free-market form of capitalism with moderate state oversight

Basel III: A global regulatory framework for more resilient banks and banking systems, Basel Committee, December 2010 (revised June 2011). 5 The Banker’s New Clothes, Anat Admati and Martin Hellwig, 2013. 4

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Introduction

3

is the desired economic framework; and that society’s capacity for risk is low enough not to accept anything like the level of risk that was building up in the system in 2005/6. Our thesis is that finance and banking are important features of a modern, democratic society and liberal, capitalist economy. But the risks that are inherent need to be well managed, regulated and supervised. They can be mitigated, never tamed but, if we adopt the wrong approaches, they can be needlessly inflamed. So we propose a vision of a banking industry that is based firmly on free-market principles but supplemented by a benign and competent public authority, which ensures that risk is transparent and confronted through rigorous and intelligent risk management capabilities. The proposals are set out in the immodestly titled Chapter 7: “A Blueprint for Basel IV”. They comprise suggestions on:

• • • • • • •

improved risk management processes, including better information and the use of dynamic “wargaming” over “stress testing”; a hands-on “Guardian Angel” approach to supervision; a more impressionistic and subjective approach to capital and funding; some radical proposals on deposit funding (effectively, the nationalisation of guaranteed deposits by the central bank) and liquidity management (replacing investments in government bonds with a central bank overdraft); increased rigour in governance processes and management accountability structures through the adoption of a meticulous “Centurion approach”; the active engagement of market forces in bank governance by means of a new “glasnost” approach; and relatively liberal and flexible common-sense views on human capital management and industry structure, which should be allowed to find their own form through market forces, good risk management and good governance.

These proposals are meant to be a “strawman”: “throw stones and it doesn’t hurt”. We have put these ideas forward because there are so few, coherent, credible responses to the lessons of the current financial crisis, even those of the most esteemed experts and banking authorities. The debate on the banking industry is polarised and not progressing at a great pace. Banks are engaged in “lobbying” to protect their vested interests; the authorities are keen to be seen as competent and in possession of the magic fix; almost everyone else is frustrated and feels disenfranchised. We do not feel that taking sides is appropriate: this is not a battle between two armies. Society needs banks, banks need to change and society needs to guide that change. There should be no opposing objectives between bankers and banked: there may be multiple viewpoints, but the objectives should be non-controversial. Status quo is not acceptable. To put it bluntly, we feel that the banking industry has still not been fixed and the current reform agenda is not going to change that. We hope we are not naïve. We are aware of some of the challenges that our proposal would entail and have dedicated Chapter 8 to the consideration of some of these challenges. The reader should be aware of some questions of style:

The subject matter is broad and raises many questions. This book skims the surface. We hope that the inquisitive reader will be left with a thirst to dig deeper into several areas.

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4

• •

Better Banking

We use quotes extensively, to demonstrate the views and nuances of experts in the industry and commentators, as there is no need to “reinvent the wheel” when others have already provided good material. Too much of the technical debate on the banking industry is inaccessible: this book hopes to be highly accessible, insofar as that is possible with a highly complex, sophisticated and, let’s face it, intangible topic. In order to improve accessibility, we have a glossary of jargon. Data exhibits are kept to a minimum and are included only where they are highly relevant. We don’t use maths beyond what’s necessary and even then, only very basics of risk management or simple sums.

Thematically, the book has three main parts. Initially, we set out our diagnostic and methodological foundations; then, we consider 14 real-life case studies; lastly, we set out for consideration the proposals for “better banking”.

1.2 QUICK START GUIDE TO BANKING CONCEPTS AND REGULATION The banking industry is huge and important. Due to its central role in the economy, it stores or handles vast sums of money. To give some idea of scale, consider the following statistics in Table 1.1. These numbers may not mean very much in isolation. But they hopefully illustrate that banks deal with big sums of money and getting it right is important. Imperfections or – worse – sloppiness are bound to have a disastrous effect. Banking is an industry that is at the heart of our capitalist system. On the one hand, banks take money in and safeguard it; on the other hand, banks provide credit for people to buy homes and companies to make investments. Banks act as a bridge between these two needs and their expertise in credit and investment management keeps both sides of the business happy, if everything is working well. Banks also provide payments services to facilitate the transfer of money for purchases, though this aspect of banking is not a focus of this book. Banking is risky and banking is about risk management. The classic bank product, a loan, consists of the up-front provision of money by the bank to the borrower, who promises to repay the debt within a certain timeframe. How can a bank be certain that the loan will be repaid? What can the bank do to ensure the loan is repaid? And what should the bank do in the event of non-repayment? On the other side of the business, how can a customer be certain that the bank will be able to honour their deposit? It is an often-overlooked fact that a bank deposit is not backed with cash on reserve or gold. Or, as was said of one of the largest and best regarded banks in the world: “Turns Out Wells Fargo Doesn’t Just Keep Your Deposits in a Stagecoach Full of Gold Ingots”.6 In fact, most of any bank’s deposit base is lent out to borrowing customers, who may or may not repay their debt. This intermediation function makes banks fragile. They rely upon the confidence and trust of those who entrust their funds to them. They need to manage their risks sufficiently well to be viable in the long term (solvent) and in the short term (liquid). So risk management rapidly 6

Dealbreaker.com, 3 January 2013.

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Introduction Table 1.1

5

Key figures on banking

Penetration

It seems – though admittedly the data is not clear – that around half of the world’s population has a bank account. One-third of small businesses have a credit line from a bank.7 Globally, there is one bank branch for every 6,000 people. This number varies from one branch for every 1,100 people in Spain to one branch for every 150,000 people in Congo.8

Size: balance The balance sheet size of the world’s largest 1,000 banks is more than $100,000bn.9 sheet Size: deposits

Deposit balances globally are reported to be 44% of world GDP,10 or around $30,000bn. In richer countries, the average is 84% of GDP.

Size: derivatives

The face value of derivatives contracts is more than $400,000bn11 (see Section 3.7 for an overview of derivatives).

Volumes

The value of all the payments transactions processed by banks is in the region of £78,000bn in the UK alone,12 or 50 times the UK’s annual GDP. If we gross that up a factor of about 30 (reflecting the UK’s share of global GDP13), then we would have an estimate equivalent to more than $3,000,000bn in payments globally. In addition to these volumes, the world’s currency markets trade some $5,000bn per day14 or $1,500,000bn per annum.

Market value

The top 55 banks in the world have a market capitalisation of $4,000bn,15 representing nearly 10% of the entire global stock market capitalisation of listed companies, which is $53,000bn.16 In China and Australia, large banks make up more than a quarter of the stock market’s value.

Profitability

The top 1,000 banks make around $700bn per year in pretax profits.17 In the USA, financial companies (including insurance companies as well as banks) made up 28% of corporate profits over the last three years.18

Big banks

Fifteen banks currently have balance sheets bigger than $2,000bn:

• • • •

Europe: Deutsche Bank, HSBC, Barclays, BNP Paribas, Crédit Agricole, Royal Bank of Scotland Japan: Mitsubishi UFJ, Mizuho, Japan Post Bank China: Industrial & Commercial Bank of China, China Construction Bank, Agricultural Bank of China, Bank of China USA: JP Morgan Chase, Bank of America.

And a further 11 have balance sheets between $1,000bn and $2,000bn:

• • •

Europe: Banco Santander, Société Générale, ING Group, Groupe BPCE, Lloyds Banking Group, UBS, UniCredit, Credit Suisse Group Japan: Sumitomo Mitsui Financial Group USA: Citigroup Inc, Wells Fargo.

8 Global Financial Development Database, World Bank, April 2013. Ibid. Top 1000 World Banks 2012, The Banker, 2 July 2012. 10 Global Financial Development Database, World Bank, April 2013. 11 Mid-Year 2012 Market Analysis, ISDA, 20 December 2012. 12 Payments Council Quarterly Statistical Report, 18 March 2013. 13 14 World Bank. BIS Quarterly Review, BIS, March 2012. 15 World’s Largest Banks 2013, relbanks.com, 25 January 2013. 16 17 World Bank. Top 1000 World Banks 2012, The Banker, 2 July 2012. 18 Table 6.16D, Bureau of Economic Analysis, 28 March 2013. 7 9

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6

Better Banking

becomes an issue of solvency capital management (being able to absorb losses when they come around, without depositors losing their money) and funding and liquidity management (being able to borrow from – and repay when requested – the bank’s depositors and creditors). This is the reason why bank regulation has been focused heavily on capital levels and liquidity measures. If things don’t go well, banks can lose confidence and suffer from bank runs, where depositors try to get their funds out; the bank simply runs out of cash to meet its obligations and is forced to close. Bank runs are fortunately rare, though the current crisis is providing several new case studies. The banking industry can also suffer from system-wide crises, the financial busts that generally follow a period of boom. If something is important yet fragile, it follows that it needs to be protected. This is why regulation and supervision are important. The regulatory discussion focuses a lot on “Basel”, the international club of banking regulators where such issues are managed. A basic understanding of the three Basel regulatory regimes (Basel I, Basel II and Basel III) will help to serve as a backdrop to the more prescriptive chapters later. Society needs to ensure that the essential functions of banks are preserved, to avoid problems such as a “credit crunch”, when certain reasonable needs of the economy cannot find financing. Society also needs a “guardian” to ensure that financial stability is preserved, the value of deposits is safeguarded and banking ethics are applied. Finally, if something is important and fragile, it needs to be well managed. This is the duty of everyone, from the regulatory and supervisory “guardians” to the owners of banks to the executives and middle-managers of banks. It is also the duty of society itself for – to adapt a well-known quotation – every society gets the banking system it deserves. The following three chapters give a brief overview of how banks contributed to the current financial crisis, what the basic concepts of banking and risk management entail and the history of banking regulation to the present day.

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Contents

Preface

xi

Acknowledgments

xix

ChAPter1 Leadership:AForceforChange

1

Napoleon—Leadership Lessons Conclusion Notes

13 17 17

ChAPter2 Leadership:FromSuccesstoFailure

25

Stan O’Neal Jimmy Cayne Dick Fuld Fred Goodwin Conclusion Notes

ChAPter3 Settingthe“toneatthetop”

28 30 33 37 42 43

47

Conclusion

60

Notes

68

Appendix3ABoardQuestionsregardingthe“toneatthetop”63 ChAPter4 ethicsinFinance

Systemic Integrity Market Integrity Regulatory Integrity Organizational Integrity Personal Integrity Four Ethical Lenses

71

72 75 78 81 81 90

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Contents

Conclusion Notes

93 94

ChAPter5 theroleoftheBoard:theoryandreality

97

Blurring of the Boundaries Role of the Chair Role of the CEO Role of Committees Why Boards Failed Conclusion

99 113 116 117 117 121

Notes

126

Appendix5AtheroleofBoardCommittees ChAPter6 Leadership,Governance,Strategy,andrisk

123

131

Choice of Strategy Avoiding Cultural Risk Failure of Effective Implementation Questions Regarding Risk Conclusion

133 144 146 148 154

Notes

170

Appendix6ABoardQuestionsregardingStrategy Appendix6BBoardQuestionsregardingrisk ChAPter7 DevelopingSuitableLeaders

156 165

175

Succession Planning Talent Management The Impact of Remuneration and Reward on the Suitability of Leaders Conclusion

175 186

Notes

203

Appendix7ABoardQuestionstoensureSuitablePeople ChAPter8 ensuringOrganizationalIntegrity

193 194

197

205

Creating a Compatible Culture Problems of Compliance Instituting Appropriate Controls Conclusion

205 209 215 223

Notes

241

Appendix8ACreatingaSuitableerMFramework

226

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Contents

ChAPter9 Governance:theWiserestraintsthatSetMenFree Why Corporate Governance Matters Three Components of Good Governance Overlaps, Underlaps, and Turf Wars Regulatory Arbitrage Based on Different Philosophies of Regulation Inadequate Sanctions and Penalties The Way Forward Conclusion Notes

ChAPter10 LeadershipwithGovernance:rebuildingtrustinBanks Leadership Alone Is Not Enough Governance Failed What Is Needed to Rebuild Trust Conclusion Notes

245

245 251 260 261 262 263 264 265

271

271 272 276 286 290

AbouttheAuthor

295

Index

297

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Preface

M

ost books on leadership and governance deal with them as if they are quite distinct and separate. Perhaps this is because writers on leadership typically are historians, successful CEOs, and consultants, or come from the HR discipline, whereas those who focus on governance tend to be lawyers and accountants by training. They therefore tend to see the world through different lenses and focus their thinking accordingly. I believe leadership is a morally neutral activity, which must be governed lest it go astray with bad results. History is full of effective leaders with followers willing to die to create the conditions their leaders want. Their followers may have been motivated to do good or evil. Which route they chose depended in large part on the moral compass of their leaders. Compare, for example, the appalling behavior of the Nazis during the Second World War or Maoist Red Guards during the Chinese Cultural Revolution with the ANC members’ forgiveness in South Africa as a result of Nelson Mandela’s extraordinary example. Thus, leadership and governance cannot, must not, be treated separately, because without governance there is nothing to prevent great leaders from becoming great bad leaders. To illustrate this point and how leadership alone is not enough, I use the case of Napoleon in the first chapter, who started so spectacularly, achieved so much for France and Europe, and yet ended up failing. In the second chapter I draw parallels between Napoleon’s career and those of four initially very successful bankers who also ended up as failures: Stan O’Neal of Merrill Lynch, Jimmy Cayne of Bear Stearns, Dick Fuld of Lehman Brothers, and Fred Goodwin of the Royal Bank of Scotland. In business, if we focus only on governance, without recognizing the leadership need to align and energize employees, we may get mediocre performance at best. Quick and decisive action in business has been the justification given in the past for combining the roles of Chairman and CEO in U.S. companies. This creates a form of dictatorship that politicians can only envy. When it works well, it produces great results, as do all benevolent dictatorships. When the incumbent is incompetent, or becomes incompetent, it is disastrous. We need governance to protect us from the follies of the incompetent but powerful leader. The answer is that in both politics and business, we must think of leadership and governance together, if we are to

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Preface

avoid bad leadership and failures of governance on the one hand; and good governance with mediocre performance on the other. I hope to show that the recent failures of corporate governance in banking were mainly failures of leadership caused by great bad leaders who were successful leaders originally, but went astray because they were not subjected to the checks and balances of good corporate governance. They had great ideas that they were able to impart to the rest of the organization; they had energy; they were able to energize their subordinates; they were able to execute and they had edge. Yet, these attributes by themselves did not protect them from ultimate failure. I believe this is the result of Lord Acton’s famous adage: “Absolute power tends to corrupt absolutely.” Without a system of governance to control the actions of leaders or CEOs, they can end up believing their own propaganda. The more successful they are initially; the greater risks they run and get away with; the greater the temptation to believe they are right and others are wrong; the greater the temptation to continue with a strategy that has gone past its sell‐by date. There is no countervailing power, no effective system of checks and balances, to suggest the time may have come for them to reconsider their assumptions or to hold them back from continuing to gamble recklessly with the future of their organizations or taking actions promoting their own interests ahead of those of the organization. To illustrate these points and to draw on the lessons learned from the rise and fall of Napoleon, I look at the cases of Merrill Lynch, Bear Stearns, Lehman Brothers, and the Royal Bank of Scotland in Chapter 2. It is the role of the banking regulators to ensure that the banking system is stable. It is the role of the securities regulators to ensure that investors are protected and have the information they need to make informed decisions on where to put their money and what to expect in return. Many banking CEOs argue that the regulatory burden resulting from changes in legislation enacted in response to failures of governance is disproportionate. It is my view that such regulation is essential, if we are to have good bank leaders who are held to account for their actions, and that the recent Global Financial Crisis proved beyond doubt that such regulation is needed to protect banks from the actions of great bad leaders. As Alan Greenspan discovered, his assumptions about how the market worked and how bankers could be relied on to police themselves proved to be wrong. If only he had remembered that the Kondratieff Wave, which deals with the credit cycles in the United States, predicts a boom and bust cycle of 40 to 60 years, he might not have been so surprised at the market’s inability to self‐regulate. Equally, if he had remembered Peter Drucker’s paraphrase of Euripides, “He whom the Gods will destroy, they first give forty years of prosperity,” he might have recognized the early warning signals

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xiii

in time. One only has to compare the superior performance of Canadian and Australian banks during the Global Financial Crisis, with their much tougher regulatory regimes, with that of U.S. or UK banks to realize regulation is in fact a necessary cost of doing business because of the need to protect the system as a whole from market failure. It is the role of the Board to help the company’s management decide on the business the company is in, its beneficiaries, and the difference the company will make to their lives with the return it can expect to earn as a result. It is also the role of the Board to decide the company’s values; its risk appetite and risk management; and its succession planning, including appointing and sacking the CEO. In doing this, it is the role of Boards to challenge CEOs’ mental models constructively, because as Peter Drucker so perceptively commented, “The biggest cause of corporate failure is the unconscious ‘mental models’ of the CEO.” Yet, as will become clear, the Boards of Merrill Lynch, Bear Stearns, Lehman Brothers, and Royal Bank of Scotland, as well as many others in banking, failed to do this for a variety of reasons, among which the most important was the dominance of the CEO— in effect, leadership without adequate governance. In the case of banks, Boards have two fiduciary duties to reconcile: first to their shareholders, but second to their depositors. This makes the role of bank Directors that much harder to fulfill. The key difference between the role of the Board and that of regulators and auditors is that Boards create or destroy value through their role in governance, whereas regulators and auditors contribute to the costs of doing business in the name of disaster prevention. Securities regulators focus in addition on the equitable distribution of the value once it has been created. In making the case why leadership and governance must be considered together, I focus on the performance aspects of corporate governance and how these create or destroy value rather than the regulatory aspects, which are, in the main, the cost of doing business.

Audience The primary audience for this book is Directors of banks. It will help them by making the case that they must not allow dynamic and successful CEOs to become overconfident or arrogant so that they persist in strategies that are excessively risky or that have passed their sell‐by date. By examining the cases of Napoleon, Stan O’Neal, Jimmy Cayne, Dick Fuld, and Fred Goodwin, they will appreciate the consequences of hubris and the failure to provide an effective counterweight to keep them on track. As a result, it will reinforce their appreciation of why they are responsible for setting the

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Preface

“tone at the top” and ensuring that their bank is firmly grounded on an ethical foundation. It will also remind them of the importance of their finding the courage to speak truth to power. It provides them with a working framework to deal with the issues they will inevitably face as they fulfill their responsibilities in the key areas of good governance: setting and reviewing strategy, managing risk, succession planning and talent management, and ensuring organizational integrity by recognizing the importance of culture, compliance, and controls. It does this by exploring what is demanded of Directors, the issues they face, and, where appropriate, provides them with suitable questions to ask management so that they can challenge constructively and thus ensure the long‐term value creation of the bank for which they are responsible. In other words, the book attempts to answer the following questions: What are Directors responsible for? Why does it matter? ■■ What are the issues? ■■ How do they trust, but verify? ■■ ■■

This allows CEOs room to exercise their remit without being second‐ guessed. By providing a series of practical questions Directors should ask, this book sets itself apart from others. The secondary audience is academics, students of governance, and writers on banking, as well as auditors, lawyers, and consulting service providers to bank Boards. This book is also useful for MBAs who are thinking of entering banks.

OverviewOfthecOntents The book is divided into 10 chapters, 5 of which have supporting appendixes. The chapters are as follows. Chapter 1: Leadership: A Force for Change argues that effective leaders are able to mobilize their followers to achieve change. However, the act of leadership is morally neutral, as the changes envisioned can be good or bad. The chapter explores the extraordinary career of Napoleon, who achieved more than any single individual in European history because of his unique legacy, both militarily and administratively, and yet failed in the end. It concludes that there are 11 lessons of good and effective leadership and that Napoleon did not meet the criteria of all of them, which is why he ultimately failed.

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xv

Chapter 2: Leadership: From Success to Failure explores the phenomenon of “Imperial CEOs” as leaders of banks. It looks briefly at the cases of the CEOs of Merrill Lynch, Bear Stearns, Lehman Brothers, and Royal Bank of Scotland to see whether there are any generalizable lessons that can be drawn from their experiences—in particular, from the ways in which they ultimately failed the 11 tests of good and effective leadership identified in Chapter 1. Chapter 3: Setting the “Tone at the Top” deals with the role of bank leaders and their Boards in setting the “tone at the top.” It explores the interaction between leaders and followers and the importance of the courage to speak truth to power if leaders are to be kept from being corrupted by the power they wield. It is particularly critical in the banking sector, where failures of “tone at the top” may have led to gigantic losses at Société Generale, UBS, and JP Morgan Chase; to the weakness of compliance and controls at HSBC; and to the LIBOR price‐fixing scandal. These failures undermine the case for self‐regulation of financial services. The chapter makes it clear that the responsibility for setting the “tone at the top” belongs with both the leadership and the Board, with the Board providing the governance to keep leaders honest. It is supplemented by the appendix “Board Questions Regarding the “Tone at the Top.” Chapter 4: Ethics in Finance explores the impact of the four types of integrity: systemic integrity, market integrity, organizational integrity, and personal integrity on ethical decision making. Building on the previous chapter “Setting the Tone at the Top,” it looks at ethical dilemmas through four separate, but interdependent lenses to provide people with tools to make ethical business decisions, recognizing that, for individuals, ethical decisions are viewed differently depending on both their cultural backgrounds and where they are in the organization. It uses simple, practical, and easy to understand ethical concepts to guide thinking and is not intended to be a deep discussion of moral philosophy. Chapter 5: The Role of the Board: Theory and Reality discusses what bank Boards are supposed to do, taking into account the underlying economic and market realities, as they affect the ability of Directors to carry out their function effectively in helping CEOs be “great good” leaders. In doing so, it discusses the role of the Board as a whole, the roles of the Chair, CEO and committees. It concludes by discussing some of the reasons why Boards failed to prevent disaster in banks in the Global Financial Crisis. The chapter is supplemented by the appendix “The Role of Board Committees.” Chapter 6: Leadership, Governance, Strategy, and Risk explores the connection between leadership, strategy, and risk and the resulting need for

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governance by the Board. Strategic choices often reflect the desires, ambitions, and personalities of the leaders who decide what the organization’s strategy should be. The link between strategy and risk is threefold: first, leaders themselves and their ambitions may pose unforeseen risks; second, the objectives of the strategy may present risks in terms of the acceptability of the organization to the society in which it operates; and third, the risks of poor implementation. This chapter is supplemented by two appendixes: “Board Questions Regarding Strategy” and “Board Questions Regarding Risk.” Chapter 7: Developing Suitable Leaders deals with the difficult topics of succession planning and talent management—an area where many leaders have failed, perhaps because of an unwillingness to recognize they are both mortal and dispensable. It discusses the suitability of talent management and the identification of key skills in which employees must be trained, given the rapidity with which the banking world changes, often rendering business models obsolete. It also explores the need to combine ever‐ greater specialization (as skills and knowledge become deeper) with the need to remain an effective generalist (able to bridge the gaps between the silos created by technical specialization) and what this means for Boards and CEOs. It discusses the often neglected importance of ensuring that the leadership cadre represents the desired values and culture, as opposed to merely having the desired technical proficiency and skills. Finally, it covers the vexed issue of remuneration, as part of ensuring that the resulting leadership behaviors are suitable. It is supplemented by the appendix “Board Questions to Ensure Suitable People.” Chapter 8: Ensuring Organizational Integrity deals with the need for organizational integrity—a function of culture, compliance, and controls all working together to achieve common behavior. It explores the problematic issues raised when the leadership team is new or not in tune with the culture of the rest of the bank. It examines the role of controls to ensure that there is compliance with appropriate regulations and codes of conduct to preserve the bank’s cultural DNA and way of doing business. Finally, it looks at the need for a proper system of controls that reconciles initiative and performance with unthinking obedience and compliance. It is supplemented by the appendix “Creating a Suitable ERM Framework.” Chapter 9: Governance: The Wise Restraints That Set Men Free explores the role of governance as a counterbalance to leadership, to help bank leaders make good decisions for sustainable results. It then examines the three components of good governance—self‐discipline, market discipline, and regulatory discipline—and their contribution to good leadership.

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It makes the case that self�discipline is by far the most important because of failures in the other two disciplines. Chapter 10: Leadership with Governance: Rebuilding Trust in Banks concludes the book by drawing the arguments of the preceding chapters together to make the case that good governance is essential for sustainable value creation, and it is needed to prevent great leaders becoming great bad leaders of banks. Without it trust in banks will not be rebuilt.

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Chapter

1

Leadership: a Force for Change

e

ffective leaders create change and are able to mobilize their followers to achieve such change. However, the act of leadership is morally neutral, as the changes can be good or bad. This chapter explores the extraordinary career of Napoleon, who achieved more than any single individual in European history, both militarily and administratively, and yet failed in the end. It concludes that there are 11 lessons of good and effective leadership and that Napoleon did not satisfy the criteria of all of them, which is why he ultimately failed. To be a leader one must have followers. History is full of leaders with followers prepared to die to achieve what their leaders asked of them or ordered them to do. The greatest leaders changed the world they lived in, both for the better and for the worse. Regardless of the outcome, what they had in common was good timing, a strong sense of purpose, and an exceptional ability to communicate their vision and harness the values of their followers to energize them to action. I believe truly great leaders are remembered because they were able to create major change, or else lasting change, or both. Perhaps the difference between truly great leaders and great bad leaders lies in their legacy and governance. I argue the great leaders of both history and business were able to build or create change that outlasted them, whereas great bad leaders manipulated their followers or employees to achieve selfish and self‐ centered goals, which did not survive their demise or led to catastrophe for their followers or employees during their lifetimes. Perhaps the best way to assess leaders as a positive force for change is to see how they have passed certain tests:1 Find the energy to create a better future. ■■ Have a clear purpose at all times. ■■ Lead with values. ■■ Encourage courage to speak truth to power. ■■

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2

Rebuilding TRusT in banks

Learn from failure and forgive and move on. ■■ Recruit co‐leaders and share authority and responsibility. ■■ Move from “I” to “We” thinking and create conditions for maximum collective success. ■■ Create a legacy that lasts. ■■

The best way to illustrate the difference between these different types of leaders, whom I define as “Great Good” leaders and “Great Bad” leaders, is shown in Table 1.1 History is so full of leaders it is difficult to know which ones to choose. To show that what we regard as great historical leadership per se is in fact morally neutral or value free and that limiting the definition of leadership to good leadership only is problematic,2 I will look briefly at one leader, Napoleon Bonaparte, as a force for change and let you decide whether he was a great good leader or whether he was, in the words of the Earl of Clarendon writing about another great revolutionary leader, Oliver Cromwell, “A brave badd [sic] man.”3 It is worth noting that some of the greatest leaders of history may not pass all these tests and many more will fail the legacy test. I refer in particular to tests 3, 4, 6, and 7 below. We may find we cannot agree with their values. They did not encourage people to speak truth to power but shot the messenger instead. We may find the real underlying motive was all about satisfying “I” and had little to do with “We”; or the spirit of the times and the style of command did not allow for maximizing collective success, tabLe 1.1

Leadership Styles Compared

Great Good Leaders

Great Bad Leaders

1. Find the energy to create a better future. 2. Have a clear purpose at all times. 3. Lead with values and by example. 4. Encourage people to speak truth to power. 5. Learn from failure. 6. Recruit co‐leaders and share authority and responsibility, while retaining accountability. 7. Move from “I” to “We” thinking and create maximum conditions for collective success. 8. Create a lasting legacy.

1. Find the energy to create change, though often not for the better. 2. Have a clear purpose at all times. 3. Lead through fear and force. 4. Shoot the messenger. 5. Paranoiacs who punish failure. 6. Centralize control and authority becoming bottlenecks in decision making. 7. “Après moi le deluge”; regard themselves as indispensable and manipulate followers. 8. Fail to create a lasting legacy.

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Leadership: A Force for Change

depending on how we define collective success. Many are likely to have been dictators, tyrants, or autocrats and, despite this, they were regarded as great leaders, even if their followers had no choice but to follow them. Great good leaders, however, did not have this problem. Their followers chose willingly to be led by them. Even so, as early as Confucius, rulers were advised to be benevolent and virtuous: He who rules by virtue is like the polestar, which remains unmoving in its mansion while all the others revolve respectfully around it.4 When asked what a ruler should do, Confucius replied: Approach them with dignity and they will be respectful. Be yourself a good son and a kind father and they will be loyal. Raise the good and train the incompetent, and they will be zealous.5 Lao Tsu, a Chinese contemporary of Confucius, recognizing there were bad leaders as well as good and great ones, had this to say about leadership: A leader is best when people barely know he exists, not so good when people obey and acclaim him, worst when they despise him. But of a good leader, who talks little, when his work is done, his aim fulfilled, they will say, “We did this ourselves.”6 As Barbara Kellerman points out in her book, Bad Leadership: What It Is, How It Happens, Why It Matters, this assumption leadership is a form of behavior that gives followers the choice whether to be led or not, is a new idea. It dates back to the work of James McGregor Burns in 1978 when he introduced the concept of transformational leadership7 and Warren Bennis in 1989 when he introduced the concept of authentic leadership.8 Both defined leadership as an exercise of power over others based on mutual advantage: “that leaders engage others by creating shared meaning, speaking in a distinctive voice, demonstrating the capacity to adapt and having integrity.”9 Leaders who coerced their followers or, worse still, obliterated them, were not leaders; they were defined as “power wielders” by Burns. “Power wielders may treat people as things; leaders may not.”10 Yet historians and political scientists throughout history before this reframing of leadership by Burns and Bennis knew about the dark side of leadership and studied it extensively and neutrally;11 nobody more so than Machiavelli in his book The Prince. He accepted the idea of coercive leadership, because in his mind, the only leader who is bad is a weak leader who

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Rebuilding TRusT in banks

cannot make things happen. So much so, that Machiavelli gives advice on how best to coerce followers: Cruelties can be called well used (if it is permissible to speak well of evil) that are done at a stroke, out of the necessity to secure oneself and then are not persisted in but are turned to as much utility for the subjects as one can. Those cruelties are badly used which, though few in the beginning, rather grow with time.12 This brings me to a fundamental issue in the discussion about leadership and its corollary, followership: why leaders lead and followers follow. At its most basic, the answer to this question is self‐interest. Leaders and followers engage in a compact designed to protect all against the anxieties caused by disorder and death. In the end, it is this that unites the thinking of Hobbes,13Locke,14 and Rousseau.15 What differentiates their positions is the emphasis they place on the obligations they believe leaders must take on if they are to maintain legitimacy in the eyes of their followers. There are many reasons why followers put up with bad leaders. At the individual level, bad leaders may satisfy a need for certainty, simplicity, and security. From childhood, we have been acculturated into followership—doing what our parents or elders tell us to. “Getting along by going along” is an important social lesson we all learn when we are young. We follow because the cost of not following is often too high. Resistance can create confusion and uncertainty, the very states most of us want to avoid, so resistance is doubly hard. We need leaders to make sense of the world, because as Nassim Taleb and Daniel Kahneman have pointed out, we do not accept the world is random.16 We need plausible causal explanations, however improbable they might be. It is the way our brains are hardwired to work.17 Leaders provide the answer to such needs. Finally, in an increasingly uncertain world, leaders are assumed to know what they are doing, even if their followers do not.18 The angst we experience when we do not understand what is happening makes us all the more likely to turn to a person who gives the appearance of being strong and certain.19 At the group level, decision making becomes even more complex. It is relatively easy for 10 people to reach a consensual decision. It is impossible for 10,000, let alone 10 million. That is why we need hierarchies with leaders at the very top of the pyramid who come to represent the whole. Such leaders have to do a great deal of demanding work—engaging stakeholders while understanding different perspectives and time horizons.20 The outcome of such work is highly uncertain and ambiguous. Most people do not want to have to deal with such ambiguity or with the anxiety caused by the fear of failure. Such people defer to those who have no such qualms, and

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they may turn out to be good or bad leaders. This tendency creates what Robert Michels termed the “Iron Law of Oligarchy,” which postulates that we naturally divide ourselves into leaders and led.21 This division of labor or specialization means that leaders get better at tolerating ambiguity and followers demand ever greater certainty, certainty that only the leader can provide. Even so, bad leaders make a compact with their followers, who in turn mold the behavior of their leaders by allowing them to behave in increasingly arbitrary and autocratic ways over time. To understand this dynamic better, we must divide followers into three groups, as Barbara Kellerman has done. Each group is quite rational in the way it accommodates evil leadership.22 First, there is the silent majority, the bystanders. They go along with what is being done because it is too much effort or too risky personally to stand up and be counted, but they do not believe in what is being proposed. They neither take part in nor stop what is being done. Second come the doers of evil—the people who follow orders because that is what they are supposed to do and take part as efficiently and effectively as they can because they are being measured and rewarded accordingly. Third, there are the acolytes, the true believers who get behind the leadership—either because they genuinely believe it is the right thing to do, or because they will get so much personal benefit from being seen to be enthusiastically aligned. In general terms, the issues of leadership are more or less the same whether I look at leadership through the historian’s, politician’s, or businessman’s lens. However, there is one area where the tools business leaders have to affect their followers differ from the tool used by politicians and military leaders. It is the ability to coerce. This is where the leadership challenge in business differs from that of political history, making it even more difficult, because business leaders cannot apply brute force to recalcitrant followers and commercial rivals, whereas political leaders can and do. What is more, business leaders must embrace change in a way that political leaders may not have to. Business is a continuous process of “creative destruction”23 because customers demand ever better products and competition springs up to provide them with what they want. Businesses that fail to adapt to the relentless twin needs—to innovate and to compete— will ultimately either be taken over or fail. In short, companies should not in fact assume “business as usual,” nor can they revert to rose‐tinted past ways of doing business. Political leaders, on the other hand, often refer to a glorious past when things were better. They also promote the value of order and stability in the name of predictability. They rarely innovate off their own bat because they

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are not faced by the twin pressures of changing customer demands and competitive offers to satisfy those demands. Sometimes, on rare occasions, they are faced with sea changes in the political landscape when citizens decide they have had enough of the prevailing form of government: The American, French, and Russian revolutions are good examples, as was the process of decolonization after World War II and perhaps the Arab Spring of 2011. The need for governments to rethink their “business model” rarely happens, unless they are defeated in war or overthrown in a revolution. Yet business leaders need to reexamine the validity of their business model at least once a year and in some fast‐moving industries more often than that. This is why I deliberately exclude all those great leaders in history who represented the forces of reaction, of conservatism, because their fundamental proposition was either defensive when faced by an existential threat (e.g., Churchill facing Hitler, Elizabeth I defending Protestant England against Philip II’s Catholic Spain) or reinstating or defending the status quo (e.g., Tokugawa Ieyasu and the Shoguns who followed him until Japan’s Meiji Restoration, or General Charles de Gaulle trying to regain a glorious position in world affairs for France after World War II). I also exclude religious leaders, however great a force for change they might be, because they are in the business of salvation—a deeply personal matter that defines individual identity. And even if business leaders promote personal and business codes of conduct like Johnson & Johnson’s celebrated Credo, they are not in the business of salvation. From a historical perspective I have chosen24 to explore briefly the career of Napoleon Bonaparte. I chose him because he was an outsider, a transformational leader who saved the French Revolution, taking France to undreamt of heights of power, whose mere presence on the battlefield was worth 40,000 men25 according to his nemesis, the Duke of Wellington. And yet he ultimately failed. However, he left behind him an unparalleled legacy, changing the nature of warfare, the political, legal, administrative, and educational systems of France and Continental Europe, as well as the political boundaries of the United States. In discussing whether leaders are bad or not, I recognize the need to define exactly what is meant by “bad.” As Barbara Kellerman points out, there are two quite distinct ways in which leaders can be bad: ineffective and unethical. She goes on to create seven categories that I use to classify leadership into ineffective or unethical bad leadership in Table 1.2. During the course of the review of both Napoleon’s and business leadership as a force for change, I refer back to these ideas in the hope that it will become obvious as a result that we need governance—a system of checks and balances—to overcome the frailties of followers and weaknesses of leaders to keep both on the straight and narrow path that defines good as opposed to effective leadership.

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tabLe 1.2

Bad Leadership Types

Ineffective Leadership

Unethical Leadership

1. Callousness: The leader and some 1. Incompetence: Leaders and followers are unkind or uncaring, followers lack the skill and/or will to ignoring the needs and wants of most sustain effective action. members of the group for which they are responsible. 2. Corruption: The leader and followers 2. Rigidity: Leaders and followers lie, cheat or steal; but above all they are stiff and unyielding. Although put self‐interest ahead of public initially competent, they are unable interest and are prepared to cloak or unwilling to adapt to new ideas their action in self‐serving hypocrisy. and circumstances. 3. Intemperance: The leader lacks self‐ 3. Insularity: The leader and some followers minimize or disregard the control and is abetted by followers impact of actions of the health and who allow self‐destructive behavior welfare of the “other”—that is, the to continue. people outside their organization who are affected by its actions; 4. Evil: The leader and some followers commit atrocities, using pain as an instrument of power, inflicting severe physical and mental harm on people. Source: Based on Barbara Kellerman, Bad Leadership: What It Is, How It Happens, Why It Matters (Boston: Harvard Business School Press, 2004), 40−46.

Obviously these dimensions are not mutually exclusive. Leaders can be both ineffective and unethical and they can combine the seven types of bad leadership to become truly awful, like Kim Jong Il of North Korea, who exhibited all seven dysfunctions. Let us now look at the case of Napoleon. He was born in the newly acquired Corsica, incorporated into France just in time for him to become the beneficiary of its revolution. He came from a family that experienced hardship as a result of his father dying when he was still young. He chose the army as a career without influential mentors or backers on whose coattails he could rise. He endured periods of disgrace in his early career when he was sent away from the centers of political power and came to prominence fighting the Royal Navy at the siege of Toulon in 1793. His mere presence on the battlefield was regarded as being decisive26 and he saved his France and its ideals from total defeat at the start of his career, though he would later lead it to total military defeat, but not to the defeat of its ideals. He had much wider interests than just war; he was a voracious reader, with insatiable curiosity and a need to learn how things worked.

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Napoleon, a Corsican speaking Italian and almost no French, was sent to boarding school in Brienne at the age of nine. There he was taught French, and indoctrinated about the greatness of France and the importance of military service and honor, beliefs that would mark him for life. His early political readings taught him France needed reform because the power of kings should be constrained. A history of England27 seems to have influenced his thinking about the nature of kingship, taking him in a radically different direction from the political thinking in continental Europe of 1785, where enlightened despots like Frederick the Great and Catherine the Great were the accepted role models.28 The Revolution did not turn out as Napoleon expected; and soon France was not only fighting other European nations who were trying to restore the monarchy, but itself as the south and other parts of France resisted the Terror led by Robespierre. Sickened by the thought of having to kill fellow Frenchmen, Napoleon pleaded to be allowed to fight the enemies of France. His wish was granted with the temporary command of the artillery at Toulon. He formulated a plan to take Toulon from the British fleet supporting British and Spanish troops garrisoned in the fort defending their positions. It succeeded. His commanding officer, Jacques Coquille Dugommier, wrote: I have no words to describe Buonaparte’s merit: much technical skill, an equal degree of intelligence and too much gallantry, there you have a poor sketch of this rare officer. . . .29 Napoleon was promoted to brigadier general. His rapid rise and his friendship with Robespierre’s brother were to cause him problems when Robespierre was executed and he was falsely accused of spying for the Genoese and placed under house arrest. He was cleared. He was, however, demoted by Aubry, a radical war minister, and had to bide his time until the day he saved the Revolution when asked by Paul Barras, “Will you serve under me? You have three minutes to decide.” Napoleon unhesitatingly answered “Yes.” Having saved the Revolution30 at the age of 26, Napoleon was promoted to full general and assumed command of the Army of the Interior. However, he was to show the world what he was made of when he was given command of the rag‐tag Army of Italy: In thirteen months Napoleon had scored a series of victories which outshone all the combined victories in Italy during the past 300 years. With an army of never more than 44,000 Napoleon had defeated forces totalling four times that number: he had won a dozen major battles, he had killed, wounded or taken prisoner 43,000 Austrians, he had captured 170 flags and 1,100 cannon. 31

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Napoleon achieved this remarkable success by combining six elements that matter in military leadership: discipline; incentives to bravery with recognition of individual and regimental success including commemorating the dead; unity of command allowing him to orchestrate his forces and reassure his troops they would not suffer from divided command; surprise achieved by flanking attacks; speed; and concentration of forces.32 Finally, as he put it himself in a letter to the Directory: “If I have won successes over forces very much superior to my own . . . it is because, confident that you trusted me, my troops have moved as rapidly as my thoughts.”33 It has also been argued that four of Napoleon’s own personal peculiarities made a difference. He had a rapid metabolism, allowing him to work very fast; he needed little sleep, surviving on half‐hour naps; he had an extraordinary feel for the countryside as a result of his upbringing in Corsica, where roads were few, mountains many, and passes critical; and he saw the world through the eyes of a gunner. He used soldiers as if they were artillery, bringing them to bear on a single point and, after taking it, moving them quickly to focus on the next point.34 As a battlefield commander, Napoleon was exceptional for a number of reasons. He was always prepared for integrated action35 and tested himself with different scenarios, always planning for the worst outcome, leaving nothing to chance, recognizing that plans had sell‐by dates. He made his troops feel they mattered at both the unit and individual levels and that he entered into a personal contract with them that brought them victories. Perhaps because he was an artilleryman and was brought up in a family of lawyers, or because of his great skill in mathematics, he realized the importance of detail, accurate firsthand information, and fact‐based analysis. Napoleon’s ability to see the big picture combined with an almost fanatical emphasis on the little picture and hypothesis testing was exceptional. His attention to detail meant he rejected executive summaries, asking for the full report instead, with specifics. He even went so far as to read the muster rolls for an hour every day to know exactly where his forces where deployed.36 Napoleon believed in the importance of good information from all sources,37 but knew it was important to consider the source carefully.38 His mathematical skill meant he was always interested in the numbers required to achieve the most effective logistics and deployment of material. He always looked for optimum performance, leading him to abandon conventional thinking about how many men were needed to execute a plan and what the best infantry firing position was. When his experiments demonstrated that the traditional three ranks firing in turn were less effective than two ranks firing at will, he wrote to General Marmont on October 13, 1813: “We believed . . . but experience has shown . . .” and abandoned the practice.39

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Napoleon held no councils of war because they lead to consensus‐based second‐best solutions.40 However, his unwillingness to hold councils of war did not mean that he did not seek other opinions. Quite the reverse; he seems to have understood clearly the dynamics of groupthink—he listened to diverse views in private,41 he wanted ideas that he could then judge for himself, and was open to ideas regardless of their origin, as was recognized by his archenemy, the Austrian ambassador, Prince von Metternich: Seizing the essential point of subjects, stripping them down of useless accessories, developing his thought and never ceasing to elaborate it till he had made it perfectly clear and conclusive, always finding the fitting word for the thing, or inventing one where the image of language had not created it, Napoleon’s conversation was ever full of interest. . . . Yet he did not fail to listen to the remarks and objections which were addressed to him . . . and I have never felt the least difficulty in saying to him what I believed to be the truth, even when it was not likely to please him.42 In the end, what set Napoleon apart from other generals, including Alexander the Great, Hannibal, and Julius Caesar, whom he admired, were the speed,43 ferocity, and tenacity with which he attacked. Everything was mobile, even artillery,44 which he kept on the move to support his infantry. The enemy was left bewildered,45 paralyzed by his unorthodox use of speed and concentration of forces to achieve an overwhelming local advantage, which he turned into battlefield victory.46 He recognized a defending army always has the advantage, and so one of his cardinal principles was to control the ground on which the battle was to be fought,47 drawing the enemy out from defensive positions and forcing them to attack him on the ground of his choice.48 His most radical innovation was that “he changed the face of warfare from the sport of kings to the nation at arms, with the whole nation being placed on a war footing, conscription, mass production and truly a nation under arms, the beginning of modern ‘Total War.’”49 Napoleon’s conscript armies were the French people at war, fighting for the glory of their country. Napoleon also deserves to be remembered for his success as a reformer. He rationalized routine government activities, reorganizing France into the 98 administrative departments it still has today, each with its own prefect, with delegated powers from Paris to decide what was best for each prefecture, applying the new civil code, or Code Napoleon,50 as it became known. The Code Napoleon of 1807 is still the law of France, Belgium, and Luxembourg. It has left its imprint on the civil laws of Germany, Holland, Italy, and Switzerland, as well as carrying its ideas of political equality and the importance of strong families as far afield as Bolivia and Japan.

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Napoleon also shaped future countries in Europe: Belgium and Holland were the result of his political administration; he resurrected a dismembered Poland by creating the Grand Duchy of Warsaw; he provided the administrative basis for the Italians51 and Germans to think of themselves as nations rather than petty principalities. In order to put France on a better financial footing and reduce his exposure to attack from the British in the Americas, in 1803, Napoleon sold France’s 828,000 square miles of land52 in North America to Thomas Jefferson for $11.25 million in cash plus $3.7 million in forgiven debts in the Louisiana Purchase. This reconfigured the United States helping it become the leading power in the world. He increased taxes, but on a rational and fair basis, and subsidized education, revolutionizing France’s secondary education system with the introduction of the lycée53 and the baccalaureat exams. Centralization and unity were key strands in Napoleon’s thinking. The Revolution stressed the importance of centralization, abolished unions, and introduced standardized weights and measures, which suited Napoleon as a benevolent dictator. What is intriguing is the importance he attached to unity. His justification for creating a single Legion d’Honneur rewarding both military and civilian excellence was that doing otherwise would split France into two camps.54 This need to preserve the unity of the French nation led Napoleon to grant an armistice to all Royalists, inviting them to return as Frenchmen to serve their country, and some 40,000 took up the offer. More important still was his decision to come to terms with the Pope by means of the Concordat of 1804. What was left of the French church had been split in two by the Revolution: those priests who swore loyalty to the Revolution and the majority who still remained loyal to the Pope. It was theoretically possible to have two churches side by side; except it went against the idea of centralization and the indivisibility of the nation. To put an end to this division and to avoid a war of religion across Europe, Napoleon agreed to a deal giving the Pope new power to depose bishops. In return, Napoleon had a clean sweep of bishops. The number of bishops was reduced to 60; they would be appointed by Napoleon, and the Pope would invest them. The State would pay the salaries of bishops and priests and place at their disposal all the unnationalized churches. Under pressure from the Council of State, which regarded the new deal as insufficiently Gallican,55 70 “organic articles” were added to the Concordat, including one asserting that the Pope must abide by the decisions of an ecumenical council. In April 1802, Napoleon reopened the churches of France—the most popular act of his rule.56 Napoleon used the opportunity to improve the quality of the priesthood and then left the church alone to act as it saw fit. The Concordat remained

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in force until 1905 and was the model for 30 similar treaties between the Vatican and foreign governments. As the Pope himself said, “The Concordat was a healing act, Christian and heroic.” Upon achieving a balanced budget for the first time since 1738 through his stiff but egalitarian tax system and thriftiness in government, supported by the establishment of the Bank of France in 1800,57 Napoleon set about building three great canals,58 three great ports,59 and three great roads across the Alps.60 Within France Napoleon spent 277 million Francs between 1804 and 1813 on roads, lined with trees to protect their users from the sun, changing the look of France forever. He was the first to pave a road in Paris and established its first professional fire brigade. He founded the Bourse (stock exchange), and the Administration des Eaux et Forets to protect the rivers and woods. Despite the wars, France enjoyed a prosperity she had not known for 130 years: People who were eating meat once a week in 1799 were eating it three times a week in 1805. When times were difficult, as in the winter of 1806−1807, Napoleon personally spent money from his privy purse to keep the silk industry in Lyon going and bought cloth from Rouen; and in 1811 he secretly advanced enough money to the weavers of Amiens to pay their workers.61 Napoleon never forgot that he had an economic contract with the people of France, and if he failed to deliver, he would be overthrown: I fear insurrection caused by a shortage of bread – more than a battle against 200,000 men.62 As a reformer, Napoleon looked into every area of policy. Initially his republican instincts guided him, though by 1804 after a number of assassination attempts he changed, making himself Emperor of the French, and putting his brothers into positions of power in Italy, Spain, and Holland in an attempt to create a dynasty to replace the exiled Bourbons. He justified this on three counts: 1. There had been attempts on his life, and so he needed to create a succession mechanism to protect the gains of the Revolution. 2. The monarchs of Europe were unwilling to accept him, and so he needed to build alliances through marriage where possible—hence his marriage to Marie Louise of Austria. 3. Last, his ex‐post justification after he was exiled for the second and last time to St. Helena: In establishing a hereditary nobility Napoleon had three aims, (1) to reconcile France with Europe, (2) to reconcile the old France with the new, (3) to wipe out in Europe the remnants of feudalism by associating the idea of nobility with that of public service and disassociating it from any feudal concept.63

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It is hard to know whether this is ex‐post special pleading. At the start of his career Napoleon had real republican instincts. After the Italian campaign, Napoleon recommended the duchy of Milan and Lombardy should be allowed to become the Cisalpine Republic, modeled on the revised, more moderate French constitution. To help their cause, Napoleon had raised a Lombard regiment that fought with distinction against Austria—the red, white, and green flag he gave them would later become the Italian national flag. The Cisalpine Republic was so appealing that territories that had been part of the Papal States asked to join, as did the Genoese. Napoleon counseled moderation to include the aristocrats who had led Genoa for centuries in his negotiations with the Genoese—supporting his claim that he was interested in reconciling the old and new worlds. He was a supporter of the Swiss Republic and the Batavian Republic. The fact that he created the Legion d’Honneur to combine the idea of nobility with the idea of service gives credence to his third claim. Clearly, however, he failed in his first aim. The English were implacably opposed to having France as the most powerful nation in Europe—maintaining the balance of power in Europe had been an English policy since 1558. The Austrian monarchy found it hard to forgive the fact the Revolution had caused the death of Marie Antoinette, a family member. Napoleon’s attempts to blockade England drove the Russians into the arms of the English, the paymasters of the six coalitions that fought against him. Moreover, the execution in 1804 of the Duc D’Enghien, falsely implicated in the failed assassination attempt in 1803 by Talleyrand, was seen by many in Europe as judicial murder. Becoming Emperor, even though it was at the request of the French people, was the last straw, alienating many of his previous supporters across Europe, including Beethoven, who changed his dedication to his great third symphony, the Eroica, “To the memory of a great man.” Perhaps his most serious error was to gamble desperately in 1814 rather than to accept the generous terms initially offered to him by Tsar Alexander II, who remained an admirer of Napoleon to the end. As a result, he faced a revolt by his marshals and was forced to abdicate.

NapoLeoN—Leadership LessoNs Earlier I quoted Barbara Kellerman’s seven traits of bad leadership and I compared great good and great bad leadership, using eight criteria. I now use these to see how Napoleon fares, shown in Table 1.3. How Napoleon fares when using the eight tests of great good leadership discussed earlier is shown in Table 1.4.

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Napoleon Evaluated against Seven Tests of Bad Leadership

Ineffective Leadership

Napoleon’s Leadership

1. Incompetence: Leaders and followers lack the skill and/or will to sustain effective action.

Napoleon was extraordinarily competent, with a grasp of the big picture and an eye for detail for both the battlefield and people.

2. Rigidity: Leaders and followers are stiff and unyielding. Although initially competent, they are unable or unwilling to adapt to new ideas and circumstances.

Napoleon innovated both on the battlefield and in developing new administrative solutions; he was willing to listen to all and surrounded himself with leading intellectuals. Most important of all, in agreeing the Concordat with the Pope, he demonstrated a willingness to compromise that saved France and Europe from yet another religious war.

3. Intemperance: The leader lacks self‐control and is abetted by followers who allow self‐destructive behavior to continue.

Napoleon does not appear to have lacked self‐control; his apparent garrulousness was a carefully practiced art to put informants at ease. His thriftiness was extreme and he was regarded as personally incorruptible.

Unethical Leadership 1. Callousness: The leader and some followers are unkind or uncaring, ignoring the needs and wants of most members of the group for which they are responsible.

Although Napoleon was extremely concerned that individual soldiers be recognized and ensured they were properly fed, he does not seem to have worried at all at the number of casualties his campaigns created. However, he was acutely conscious of the need to improve the living standards of the French people and did everything possible to help in times of trouble.

2. Corruption: The leader and followers lie, cheat, or steal; above all, they put self‐interest ahead of public interest and are prepared to cloak their action in self‐serving hypocrisy.

Even though Napoleon changed the rules and made himself Emperor of the French, he did not profit personally from his exalted position. Nobody ever accused him of corruption.

3. Insularity: The leader and some followers minimize or disregard the impact of actions regarding the health and welfare of the “other”—that is, the people who are outside their organization but are affected by its actions;

Napoleon had a global mind-set, though he does not seem to have understood that the English would never make peace with him for both personal reasons and reasons of realpolitik—dismissing them as a “nation of shopkeepers” was to underestimate their staying power.

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Unethical Leadership 4. Evil: The leader and some followers commit atrocities, using pain as an instrument of power, inflicting severe physical and mental harm on people.

tabLe 1.4

Napoleon’s violence and use of terror was carefully calibrated to meet the circumstances in which he found himself (thus passing Machiavelli’s test). On several key occasions, he showed great leniency toward his enemies and pardoned two who had betrayed him, following in the footsteps of Roman emperor Augustus, whom he admired.

Napoleon Evaluated against Eight Tests of Great Good Leadership:

Eight Tests of Great Good Leadership

Napoleon’s Performance

1. Find the energy to create a better future.

From the outset, Napoleon wanted to change France for the better. This desire stemmed from the way the officials of the Ancien Regime treated his widowed mother with disdain. He believed the monarchy should be made accountable to the people and regarded himself as having an economic and social contract with the people of France. “All my life I have sacrificed everything, tranquility, interest, happiness, to my destiny.”64

2. Have a clear purpose at all times.

Napoleon put the unity and greatness of France above everything else: “I am destined to change the face of the world; at any rate this is my belief.”65

3. Lead with values and by example.

Napoleon had moderate habits and made moderation a cardinal principle of his politics.66 Napoleon believed in cleanliness and clean government—nobody ever approached him to bribe him. He believed in the love of honor and the love of the France. He was a workaholic,67 meticulous in his attention to detail, and was an attentive listener, open to the ideas of others. As a soldier, he lived with his troops, sharing their conditions.68 There were, however, two exceptions to his belief in basic equality: He did not believe in the rights of women and he reintroduced slavery into Haiti, provoking a rebellion that he was unable to put down.

4. Know how to There are three areas where it seems Napoleon did not learn manage grief and from failure: (1) failing to recognize that England was not learn from failure. going to make peace with him and should be taken more seriously; (2) allowing the war in the Iberian Peninsula to continue unabated, leaving him to wage war on two fronts;

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(Continued)

Eight Tests of Great Good Leadership

Napoleon’s Performance and (3) not coming to terms with Tsar Alexander when he still had the chance in 1814. He did, however, learn from his failure in Haiti that France could not defend its interests in the Western Hemisphere and sold Louisiana to Jefferson as a result.

5. Forgive and move on.

Napoleon offered generous terms to the Austrians and Italians at the end of the Italian campaign. He forgave two of his most serious enemies. His willingness to settle with the Pope shows an ability to move on when necessary. His sale of Louisiana shows that he sometimes understood the need to cut his losses while he was ahead.

6. Recruit co‐ leaders and share authority and responsibility, while retaining accountability.

Napoleon was a firm believer in meritocracy in administration and in military matters. He was willing to consult extensively in diplomatic and administrative matters, overseeing dramatic improvements. In military matters, he was not willing to share authority, arguing that it was his job and his alone to ensure the war was prosecuted effectively. Napoleon honed the corps system of army groups so they could function completely independently with their own logistics, scouts, command, and artillery.

7. Move from “I” to “We” thinking and create maximum conditions for collective success.

He wanted to make his generals so successful they would never dishonor their profession.69 However, his marshals do not seem to have been capable of truly independent thought, still requiring his coordination and vision—leading to defeat in Spain and finally at Waterloo.70 Initially Napoleon’s republican instincts supported collective success. However, once he became Emperor, he gradually became more preoccupied with his personal status and position at the expense of France, ultimately leading to the defection in 1814 of his marshals, who forced him to abdicate.71

8. Leave a lasting legacy.

Napoleon changed the nature of war, introducing “Total War.” He replaced a corrupt rent‐seeking Ancien Regime with a meritocratic system in the military, the church, and the civil service. He created a civil code that was to become the law of France, Belgium, and Holland and influential in Germany and Italy and as far away as Japan and Bolivia.

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He put French finances back on a sound footing. He invested in upgrading France’s national infrastructure, changing the look of France and incidentally changing how people traveled, from the left to the right side of the road in the countries he conquered. He decentralized government through the creation of semi‐autonomous prefects who “ruled” their departments and unified the educational system by introducing professionally qualified teachers of a centralized curriculum taught across France in lycées, culminating in the Baccalaureat exam. Finally he laid the administrative and political infrastructure for the creation of Belgium, Netherlands, Poland, Germany, and Italy, as well as the modern United States through the Louisiana Purchase.

CoNCLusioN There are 11 tests of great good leadership. Napoleon did not pass all of them, which is why he ultimately failed. However, he did so well on the tests that he did pass that it is not surprising that Napoleon is regarded as one of the greatest and most effective leaders of all time—the only man in history to have had wars named after him—leaving behind an unparalleled legacy. Whether he qualifies as a great good leader or as a great bad man, I leave to you to decide, based on the evidence in this chapter. What I do believe is that there are important lessons for bank leaders to learn from his experience if they are to rebuild trust in their organizations.

Notes 1. I am indebted to my colleague Rajeev Peshawaria for the first seven tests, which come from his book, Too Many Bosses, Too Few Leaders (New York: Free Press, 2011). They were summarized in an article on August 22, 2011 by Jason Moser in Motley Fool entitled “Lessons for Leaders.” 2. Redefining leadership as being good leadership creates three major problems: 1. It is confusing because for most people the word leader refers to “any individual who uses power, authority and influence to get others to go along. When we talk of evil political leaders like Hitler, Stalin, and Mao, we still call them ‘leaders’ and not ‘power wielders.’” 2. It is misleading since “leadership can be considered the exercise of influence, or a power relation, or an instrument of goal achievement, or a differentiated role. The point is that each of these definitions is value‐free. It makes

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3.

4. 5. 6. 7. 8. 9. 10. 11.

12. 13. 14. 15. 16.

17. 18. 19. 20.

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no sense therefore to distinguish between leaders and power‐wielders. In fact, to compare them is not to compare apples and oranges, but apples and apples.” 3. It does a disservice since we need to learn about good leadership by studying both what makes good leaders and what makes bad leaders so that we can avoid their mistakes. Barbara Kellerman, Bad Leadership: What It Is, How It Happens, Why It Matters (Boston: Harvard Business School Press, 2004), 13. “In a word, as he was guilty of many crimes against which Damnation is denounced, and for which hell‐fire is prepared, so he had some good qualities which have caused the memory of some men in all Ages to be celebrated; and he will be look’d upon by posterity as a brave badd man.” Edward Hyde, Earl of Clarendon, in History of the Rebellion, Macray, W. D., ed. (1888), III, vii, 84, cited in The Dictionary of Biographical Quotations. Confucius, Analects of Confucius (New York: Norton, 1997), 6. Ibid., 8. www.brainyquote.com/quotes/authors/l/lao_tzu.html, accessed on 18 July 2013. James McGregor Burns, Leadership (New York: HarperCollins, 1978). Warren Bennis, On Becoming a Leader (New York: Basic Books, 1989). Kellerman, Bad Leadership, 9. Ibid., 8. “Historically, political theorists have been far more interested in the question of how to control the proclivities of bad leaders than in the question of how to promote the virtues of good ones. Influenced by religious traditions that focused on good and evil, and often personally scarred by war and disorder, the best political thinkers have had a rather jaundiced view of human nature.” Kellerman, Bad Leadership, 5. Niccolo Machiavelli, The Prince (Chicago: University of Chicago Press, 1998), 37, 38. Thomas Hobbes, Leviathan (1651). John Locke, Second Treatise on Civil Government (1690). Jean‐Jacques Rousseau, The Social Contract, or Principles of Political Right (1762). Nicholas N. Taleb, Fooled by Randomness: The Hidden Role of Chance in the Markets and Life (New York: W. W. Norton, 2001); Nicholas N. Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2010). Daniel Kahneman, Thinking, Fast and Slow (London: Allen Lane, 2011). Stanley Milgram, Obedience to Authority: An Experimental View (New York: Harper & Row, 1974). Jean Lipman‐Blumen, “Why Do We Tolerate Bad Leaders—Magnificent Uncertitude, Anxiety and Meaning,” in The Future of Leadership, Warren Bennis et al., eds. (San Francisco: Jossey‐Bass, 2001). Elliot Jacques, “In Praise of Hierarchy,” Harvard Business Review, January 1990.

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Leadership: A Force for Change

19

21. Robert Michels, Political Parties (New York: Free Press, 1962), 66. 22. Kellerman, Bad Leadership, 25–27. 23. Joseph Schumpeter, Capitalism, Socialism, and Democracy (London: George Allen and Unwin, 1943). 24. I could have chosen many other names, such as the first Chinese emperor Qin Shi Huang, Mao Zedong or Deng Xiao Ping from China; Genghis Khan and Kublai Khan from Mongolia; from England, William the Conqueror, Elizabeth l, Oliver Cromwell, and Winston Churchill; from France, Joan of Arc and Louis XIV; from India, the emperors Asoka and Akbar the Great, and Mahatma Gandhi; from Germany, Charlemagne, Charles the Fifth, Frederick the Great, and Adolf Hitler; from Rome, Julius Caesar and the emperors Augustus, Hadrian, Trajan, and Constantine; from Russia, Alexander Nevsky, Ivan the Terrible, Peter the Great, Catherine the Great, and Stalin; from the United States, George Washington, Abraham Lincoln, Franklin Roosevelt, and Martin Luther King; from Vietnam, Ho Chi Minh; from South Africa, Cetewayo and Nelson Mandela; from Turkey, Suleiman the Magnificent and Ataturk; and from Carthage, Hannibal. The fact I did not choose them should not be taken as an adverse comment on their greatness and significance. If you prefer them or others from the huge list of famous people, then I suggest you do a similar exercise of evaluation to the one in this chapter and see for yourself what you discover. 25. Duke of Wellington on Napoleon’s death: “I used to say of Napoleon that his presence on the field made the difference of forty thousand men.” Elizabeth Knowles, The Oxford Dictionary of Quotations: Major Edition (Oxford, England: Oxford University Press, 1999), 809, #19. 26. Ibid. 27. John Barrow, A New and Impartial History of England from the Invasion of Julius Caesar to the Signing of Preliminaries of Peace, in the Year 1762. 28. “What was wrong with France, Napoleon decided, was that the power of the King and the King’s men had grown excessive; the reform Napoleon wanted— and the point is important in view of his future career—was a constitution which, by setting out the people’s rights, would ensure that the King acted in the interest of France as a whole.” Vincent Cronin, Napoleon (London: Collins, 1979), 48–49. 29. Jacques Coquille Dugommier’s dispatch to the minister of War, quoted in Cronin, Napoleon, 77. 30. On the 13th Vendemmiare Napoleon had to defend the Tuileries, the seat of government from an expected rebel attack of 30,000 men, while the government only had 5,000 troops and 3,000 militiamen. Guns were to be the deciding factor. Napoleon had eight 8‐pounders, which he loaded with grapeshot. Firing accurately into the rebels, Napoleon broke the attack and saved the Revolution. Cronin, Napoleon, 85. 31. Cronin, Napoleon, 126–127. 32. Cronin, Napoleon, 127–128. 33. Cronin, Napoleon, 128 34. Cronin, Napoleon, 114.

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35. Military Maxim VII. Accessed February 15, 2013, www.military‐info.com/ freebies/maximsn.htm. 36. I am indebted to Alan Axelrod for an excellent review of what made Napoleon such an effective leader in his book Napoleon, CEO (New York: Sterling, 2011). 37. Military Maxim LXXVI. Accessed February 15, 2013, www.military‐info.com/ freebies/maximsn.htm. 38. Military Maxim LXIII. Accessed February 15, 2013, www.military‐info.com/ freebies/maximsn.htm. 39. “He concluded that, in actual combat, firing at will, rather than in simultaneous mechanical volleys, had become more effective. He also concluded that having the third rank fire over the shoulder of the second rank was hazardous to the second as well as first ranks. It was decided to reduce the firing ranks and to assign the third rank to the task of reloading the weapons of the front two. Napoleon and others “believed” that this would increase the volume of fire, but “experience” soon showed that the second rank fired no more rapidly using this method than it did before, and, furthermore, it fired less accurately. Napoleon therefore decided to do away with the three‐rank firing formation altogether.” Alan Axelrod, Napoleon: CEO, 160. At first he (and others) experimented with two ranks, but Napoleon ultimately concluded that the “fire of skirmishers—that is, accurate firing at will against specific targets—is best of all,” with the volley fire of a single rank second best, and that of two ranks “still good.” He accordingly changed the way large infantry formations fired their shoulder weapons.” Alan Axelrod, Napoleon: CEO, 161. 40. Military Maxim LXV. Accessed February 15, 2013, www.military‐info.com/ freebies/maximsn.htm. 41. “Never hold a council of war, but listen to the views of each in private.” Letter to Joseph Bonaparte, January 12, 1806, cited in Axelrod, Napoleon: CEO, 110. 42. Prince von Metternich quoted by Felix Markham, Napoleon (New York: Penguin Books, 1963), cited in Axelrod, Napoleon: CEO, 200. 43. “It is said that the Roman legions marched twenty‐four miles a day; our brigades have marched thirty while also fighting.” Letter to the Directory, January 18, 1797, cited in Axelrod, Napoleon: CEO, 188. 44. “Never forget that in war all the artillery must be with the army and not in the park.” Letter to General Clarke, January 18, 1814, cited in Axelrod, Napoleon: CEO, 84 45. “We no longer understand anything; we are dealing with a young general who is sometimes in front of us, sometimes in our rear, sometimes in our flanks; one never knows how he is going to deploy himself. This kind of warfare is unbearable and violates all customary procedures.” Hungarian officer captured at the Battle of Lodi speaking to Napoleon, whom he did not recognize. Cited in Axelrod, Napoleon, CEO, 145. Also see Military Maxim XX, accessed February 15, 2013, www.military info/freebies/maximsn.htm. 46. “There is a moment in every battle at which the least manoeuvre is decisive and gives superiority, as one drop of water causes overflow.” Napoleon in St. Helena,

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Leadership: A Force for Change

47. 48. 49. 50.

51.

52.

53.

54.

21

quoted by Felix Markham, Napoleon (New York: Penguin Books, 1963), cited by Axelrod, Napoleon: CEO 174. Military Maxim XVI. Accessed February 15, 2013, www.military‐info.com/ freebies/maximsn.htm. “If the enemy occupies a strong position you must occupy a position that will force him to attack you.” Quoted in Luvaas, J., Napoleon on the Art of War (1999), cited in Axelrod, Napoleon, CEO 163. “Napoleon Bonaparte (1769–1821),” www.historyofwar.org/articles/people_ napoleon.html, accessed January 1, 2012. Under the Code all people were declared equal under the law and special privileges for the nobility and the church were abolished, as were feudal rights. Religious freedom was guaranteed, as was trial by jury (though this was later revoked). Parents were given rights over their children for the first time. More contentiously, wives were not allowed to sell or give away their property and could only own property with their husbands’ consent in writing and fathers were allowed to imprison their children for up to one month. He succeeded at Campo Formio in getting the Austrians to recognize his fledgling republics, but had to capture Venice and trade it to the Austrians to get them to agree to give up Milan. These actions laid the foundations for the reunification of Italy in 1871. The land that was bought enclosed all of Arkansas, Missouri, Iowa, Oklahoma, Kansas, and Nebraska as well as parts of Minnesota, North Dakota, South Dakota, New Mexico, Texas, Montana, Wyoming, Colorado, and of course Louisiana. The land purchased also included parts of what is now Alberta and Saskatchewan in Canada. The land purchased in the Louisiana Purchase now makes up about 23 percent of the territory of the United States. www.surfnetkids.com/ go/66/ten‐facts‐about‐the‐louisiana‐purchase/, accessed January 3, 2012. The key was the establishment of 30 lycées, which provided educational opportunities beyond the secondary schools and replaced the écoles centrales. Every appeal court district was to have a lycée, and they were to be completely supported, and controlled, by the state. Scholarships were provided, with about one‐third going to sons of the military and government, and the rest for the best pupils from the secondary schools. Howard C. Barnard, Education and the French Revolution (Cambridge, England: Cambridge University Press, 1969), cited by www.napoleon‐ series.org/research/society/c_education.html#19, accessed January 3, 2012. The lycées had a six‐year term of study, building on the work of the secondary schools. The curriculum included languages, modern literature, science, and all other studies necessary for a “liberal” education. Each lycée was to have at least eight teachers, as well as three masters (a headmaster, an academic dean, and a bursar). The government provided a fixed salary for teachers, as well as bonuses for successful teachers who also received a pension. Teachers were chosen by Napoleon from a list of recommendations provided by inspectors and the Institute. The inspectors were given overall responsibility for inspecting the schools on a regular basis. “If we make a distinction between military and civil honours we shall be instituting two orders, whereas the nation is one. If we award honours only to

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22

55.

56. 57. 58.

59. 60. 61. 62. 63. 64. 65. 66. 67.

68.

69. 70.

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soldiers, that will be worse, for then the nation will cease to exist.” Quoted by Cronin, Napoleon, 206. Gallicanism tended to restrain the Pope’s authority in favor of that of bishops and the people’s representatives in the state, or the monarch. These opinions were restricted to France originating in the Declaration of the Clergy of France in 1682. A. Degert, cited by C. G. Herbermann et al., ed., “Gallicanism,” Catholic Encyclopedia (New York: Robert Appleton Company, 1913). Cronin, Napoleon, 216–217. Marjorie Bloy, “The Age of George III,” www.historyhome.co.uk/c‐eight/france/ napfra.htm, accessed January 6, 2012. The Saint‐Quentin; the canal from Nantes to Brest; and the canal linking the Rhone to the Rhine, allowing him to ship goods from Amsterdam to Marseille and from Lyon to Brest without their being exposed to the guns of the Royal Navy. Cherbourg, Brest, and Antwerp. Napoleon had roads blasted through the Great St. Bernard, the Little St. Bernard and the Col de Tende. They were so well built that it became possible to travel freely between Italy, France, and Switzerland for the first time, even in winter. Cronin, Napoleon, 207–208. Quoted in Felix Markham, Napoleon (New York: Penguin Books, 1963), cited in Axelrod, Napoleon, CEO, 40. Napoleon’s third-person memorandum dictated in St. Helena after 1815, cited in Axelrod, Napoleon, CEO, 218. Napoleon to Josephine, March 1807, quoted in Norman Mackenzie, The Escape from Elba: The Fall and Flight of Napoleon 1814–1815 (Oxford, England: Oxford University Press, 1982), title page. To Joseph Bonaparte, quoted in Markham, Napoleon, cited in Axelrod, Napoleon, CEO, 39. “Moderation is the basis of morality and a man’s most important virtue . . . without it a faction can exist but never a national government.” Quoted in Cronin, Napoleon, 190. “When his physician told him he was overworking, Napoleon replied, ‘The ox has been harnessed; now it must plough.’ . . . By those in his administration, this apparently superhuman effort was applauded, by royalists abroad derided. La Chaise remarked, with a touch of adulation, ‘God made Bonaparte, and then rested.’ To which the émigré Comte de Narbonne retorted: ‘God should have rested a little earlier.’” Cronin, Napoleon, 194. General Doppet reporting on Napoleon at the 1793 Battle of Toulon, quoted by Robert Asprey, Rise of Napoleon Bonaparte (New York: Basic Books, 2001), cited in Axelrod, Napoleon, CEO, 180, and letter to Joseph Bonaparte, June 26, 1806, quoted in Axelrod, Napoleon, CEO, 154. Letter to Joseph Bonaparte, 1808, quoted by Markham, Napoleon, cited in Axelrod, Napoleon, CEO, 202. “Napoleon Bonaparte (1769–1821), www.historyofwar.org/articles/people_ napoleon.html, visited January 1, 2012.

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71. “The need for rest was so universally felt through every class of society, and in the army, that peace at any price had become the ruling passion of the day; and the whole force of this sentiment was levelled at Emperor Napoleon, whom they accused of having rejected peace, of not honestly desiring it even now. As such feelings spread Napoleon’s power inevitably crumbled. The men who had been driven so long by his restless will, and bewitched by his sense of destiny, were coming to the last breaking‐point, and he could no longer depend on any of them to act for him or to share his magical dreams as they faded in the harsh light of defeat. The most his closest comrades‐in‐arms could now do for him was to strike the best possible bargain for his life and liberty.” N. Mackenzie, The Escape from Elba: The Fall and Flight of Napoleon 1814–1815 (Oxford, England: Oxford University Press, 1982), 13.

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Contents Preface

xiii

Acknowledgments

xvii

About the Author

xix

CHAPTER 1 Some Correlation Basics: Properties, Motivation, Terminology 1.1 1.2 1.3

What Are Financial Correlations? What Is Financial Correlation Risk? Motivation: Correlations and Correlation Risk Are Everywhere in Finance 1.3.1 Investments and Correlation 1.3.2 Trading and Correlation 1.3.3 Risk Management and Correlation 1.3.4 The Global Financial Crisis of 2007 to 2009 and Correlation 1.3.5 Regulation and Correlation 1.4 How Does Correlation Risk Fit into the Broader Picture of Risks in Finance? 1.4.1 Correlation Risk and Market Risk 1.4.2 Correlation Risk and Credit Risk 1.4.3 Correlation Risk and Systemic Risk 1.4.4 Correlation Risk and Concentration Risk 1.5 A Word on Terminology 1.6 Summary Appendix 1A: Dependence and Correlation Dependence Correlation Independence and Uncorrelatedness Appendix 1B: On Percentage and Logarithmic Changes

1 1 2 5 6 8 14 18 23 24 24 25 27 30 33 34 35 35 36 37 38

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CONTENTS

Practice Questions and Problems References and Suggested Readings

CHAPTER 2 Empirical Properties of Correlation: How Do Correlations Behave in the Real World? How Do Equity Correlations Behave in a Recession, Normal Economic Period, or Strong Expansion? 2.2 Do Equity Correlations Exhibit Mean Reversion? 2.2.1 How Can We Quantify Mean Reversion? 2.3 Do Equity Correlations Exhibit Autocorrelation? 2.4 How Are Equity Correlations Distributed? 2.5 Is Equity Correlation Volatility an Indicator for Future Recessions? 2.6 Properties of Bond Correlations and Default Probability Correlations 2.7 Summary Practice Questions and Problems References and Suggested Readings

39 40

43

2.1

CHAPTER 3 Statistical Correlation Models—Can We Apply Them to Finance? 3.1

A Word on Financial Models 3.1.1 The Financial Model Itself 3.1.2 The Calibration of the Model 3.1.3 Mindfulness about Models 3.2 Statistical Correlation Measures 3.2.1 The Pearson Correlation Approach and Its Limitations for Finance 3.2.2 Spearman’s Rank Correlation 3.2.3 Kendall’s t 3.3 Should We Apply Spearman’s Rank Correlation and Kendall’s t in Finance? 3.4 Summary Practice Questions and Problems References and Suggested Readings

CHAPTER 4 Financial Correlation Modeling—Bottom-Up Approaches 4.1

Correlating Brownian Motions (Heston 1993) 4.1.1 Applications of the Heston Model

43 46 47 50 51 52 53 54 55 55

57 57 58 59 60 60 60 62 64 65 66 67 68

69 69 72

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4.2

The Binomial Correlation Measure 4.2.1 Application of the Binomial Correlation Measure 4.3 Copula Correlations 4.3.1 The Gaussian Copula 4.3.2 Simulating the Correlated Default Time for Multiple Assets 4.3.3 Finding the Correlated Default Time in a Continuous Time Framework Using Survival Probabilities 4.3.4 Copula Applications 4.3.5 Limitations of the Gaussian Copula 4.4 Contagion Correlation Models 4.5 Summary Appendix 4A: Cholesky Decomposition Example: Cholesky Decomposition for Three Assets Appendix 4B: A Short Proof of the Gaussian Default Time Copula Practice Questions and Problems References and Suggested Readings

CHAPTER 5 Valuing CDOs with the Gaussian Copula—What Went Wrong? CDO Basics—What Is a CDO? Why CDOs? Types of CDOs 5.1.1 What Is a CDO? 5.1.2 Why CDOs? 5.1.3 Types of CDOs 5.2 Valuing CDOs 5.2.1 Deriving the Default Probability for Each Asset in a CDO 5.2.2 Deriving the Default Correlation of the Assets in a CDO 5.2.3 Recovery Rate 5.3 Conclusion: The Gaussian Copula and CDOs—What Went Wrong? 5.3.1 Complexity of CDOs 5.3.2 The Gaussian Copula Model to Value CDOs 5.4 Summary Practice Questions and Problems References and Suggested Readings

72 73 74 76 81 82 85 85 88 90 91 92 93 93 94

101

5.1

101 101 102 103 105 106 110 113 113 114 114 115 116 117

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CONTENTS

CHAPTER 6 The One-Factor Gaussian Copula (OFGC) Model—Too Simplistic? The Original One-Factor Gaussian Copula (OFGC) Model 6.2 Valuing Tranches of a CDO with the OFGC 6.2.1 Randomness in the OFGC Model 6.3 The Correlation Concept in the OFGC Model 6.3.1 The Loss Distribution of the OFGC Model 6.3.2 The Tranche Spread–Correlation Relationship 6.4 The Relationship between the OFGC and the Standard Copula 6.5 Extensions of the OFGC 6.5.1 Further Extensions of the OFGC Model: Hybrid CID–Contagion Modeling 6.6 Conclusion—Is the OFGC Too Simplistic to Evaluate Credit Risk in Portfolios? 6.6.1 Benefits of the OFGC Model 6.6.2 Limitations of the OFGC Model 6.7 Summary Practice Questions and Problems References and Suggested Readings

119

6.1

CHAPTER 7 Financial Correlation Models—Top-Down Approaches Vasicek’s 1987 One-Factor Gaussian Copula (OFGC) Model Revisited 7.2 Markov Chain Models 7.2.1 Inducing Correlation via Transition Rate Volatilities 7.2.2 Inducing Correlation via Stochastic Time Change 7.3 Contagion Default Modeling in Top-Down Models 7.4 Summary Practice Questions and Problems References and Suggested Readings

121 122 127 128 129 130 131 132 134 135 135 136 138 139 140

143

7.1

CHAPTER 8 Stochastic Correlation Models 8.1 8.2 8.3

144 146 146 148 150 153 154 154

157

What Is a Stochastic Process? Sampling Correlation from a Distribution (Hull and White 2010) Dynamic Conditional Correlations (DCCs) (Engle 2002)

157 159 160

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8.4

Stochastic Correlation—Standard Models 8.4.1 The Geometric Brownian Motion (GBM) 8.4.2 The Vasicek 1977 Model 8.4.3 The Bounded Jacobi Process 8.5 Extending the Heston Model with Stochastic Correlation (Buraschi et al. 2010; Da Fonseca et al. 2008) 8.5.1 Critical Appraisal of the Buraschi et al. (2010) and Da Fonseca et al. (2008) Model 8.6 Stochastic Correlation, Stochastic Volatility, and Asset Modeling (Lu and Meissner 2012) 8.6.1 Asset Modeling 8.7 Conclusion: Should We Model Financial Correlations with a Stochastic Process? 8.8 Summary Practice Questions and Problems References and Suggested Readings

CHAPTER 9 Quantifying Market Correlation Risk 9.1 9.2

The Correlation Risk Parameters Cora and Gora Examples of Cora in Financial Practice 9.2.1 Option Vanna 9.2.2 Option Cora and Gora 9.3 Cora and Gora in Investments 9.4 Cora in Market Risk Management 9.4.1 Gap-Cora 9.5 Gora in Market Risk Management 9.6 Summary Practice Questions and Problems References and Suggested Readings

CHAPTER 10 Quantifying Credit Correlation Risk 10.1 10.2 10.3

Credit Correlation Risk in a CDS Pricing CDSs, Including Reference Entity–Counterparty Credit Correlation 10.2.1 The Model Pricing CDSs, Including the Credit Correlation of All Three Entities 10.3.1 The Model 10.3.2 Cora for CDSs 10.3.3 Gora for CDSs

162 163 165 165 168 171 172 174 176 177 177 178

181 182 184 184 185 187 189 196 197 198 199 200

201 203 205 206 215 216 223 225

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10.4

Correlation Risk in a Collateralized Debt Obligation (CDO) 10.4.1 Types of Risk in a CDO 10.4.2 Cora of a CDO 10.4.3 Gora of a CDO 10.5 Summary Practice Questions and Problems References and Suggested Readings

CHAPTER 11 Hedging Correlation Risk

235

11.1 11.2 11.3

What Is Hedging? Why Is Hedging Financial Correlations Challenging? Two Examples to Hedge Correlation Risk 11.3.1 Hedging CDS Counterparty Risk with a Correlation-Dependent Option 11.3.2 Hedging VaR Correlation Risk with a Correlation Swap 11.4 When to Use Options and When to Use Futures to Hedge 11.5 Summary Practice Questions and Problems References and Suggested Readings

CHAPTER 12 Correlation and Basel II and III 12.1 12.2 12.3 12.4 12.5

12.6

227 227 229 230 231 232 233

235 238 239 239 244 247 248 249 249

251

What Are the Basel I, II, and III Accords? Why Do Most Sovereigns Implement The Accords? Basel II and III’s Credit Value at Risk (CVaR) Approach 12.2.1 Properties of Equation (12.7) Basel II’s Required Capital (RC) for Credit Risk 12.3.1 The Default Probability–Default Correlation Relationship Credit Value Adjustment (CVA) Approach without Wrong-Way Risk (WWR) in The Basel Accord Credit Value Adjustment (CVA) with Wrong-Way Risk in the Basel Accord 12.5.1 How Do Basel II and III Quantify Wrong-Way Risk? How Do the Basel Accords Treat Double Defaults? 12.6.1 Substitution Approach

251 252 257 258 259 261 264 268 269 269

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Contents

12.6.2 Double Default Approach 12.7 Debt Value Adjustment (DVA): If Something Sounds Too Good to Be True . . . 12.8 Funding Value Adjustment (FVA) 12.9 Summary Practice Questions and Problems References and Suggested Readings

CHAPTER 13 The Future of Correlation Modeling Numerical Finance: Solving Financial Problems Numerically with the Help of Graphical Processing Units (GPUs) 13.1.1 GPU Technology 13.1.2 A GPU Model for Valuing Portfolio Counterparty Risk 13.1.3 BeneďŹ ts of GPUs 13.1.4 Limitations of GPUs 13.2 New Developments in ArtiďŹ cial Intelligence and Financial Modeling 13.2.1 Neural Networks 13.2.2 Fuzzy Logic 13.2.3 Genetic Algorithms 13.2.4 Chaos Theory 13.2.5 Bayesian Probabilities 13.3 Summary Practice Questions and Problems References and Suggested Readings

270 274 276 278 280 280

283

13.1

283 284 285 285 287 287 287 290 290 291 295 298 300 300

Glossary

303

Index

315

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Preface

C

orrelation risk is the risk that the correlation between two or more financial variables changes unfavorably. Correlation risk was highlighted in the global financial crisis in 2007 to 2009, when correlations between many financial variables such as the default correlation between debtors or the default correlation between a debtor and an insurer increased dramatically. This led to huge unexpected losses for many financial institutions, which in part triggered the global financial crisis. This book is the first to address financial correlation risk in detail. In Chapter 1, we introduce the basic properties of correlation risk, before we show in Chapter 2 how correlations behave in the real world. We then discuss whether correlation risk can be quantified using standard statistical correlation measures such as Pearson’s r, Spearman’s rank correlation coefficient, and Kendall’s t in Chapter 3. We address specific financial correlation measures in Chapter 4, and discuss whether the copula correlation model is appropriate to measure financial correlations in Chapter 5. Often, as in the Basel III framework, a shortcut to the Gaussian copula is applied, such as the one-factor Gaussian copula (OFGC) model. This approach, which is applied in the Basel framework to derive credit risk, is discussed in Chapter 6. In Chapter 7 we address a fairly new correlation family, the elegant but somewhat coarse top-down correlation models. Chapter 8 discusses stochastic correlation models, which are a new and promising way to model financial correlations. In Chapters 9 and 10, we introduce new concepts to quantify market and credit correlation risk. In Chapter 11 we address the challenging task of hedging correlation risk. Chapter 12 evaluates the proposed correlation concepts in the Basel III framework, which are designed to mitigate correlated credit and market risk. Chapter 13 deals with the future of correlation modeling, which may include neural networks, fuzzy logic, genetic algorithms, chaos theory, and combinations of these concepts. Figure P.1 gives an overview of the main correlation models that will be addressed in this book. We will discuss the conceptual, mathematical, and computational properties of the models and evaluate their benefits and limitations for finance.

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PREFACE

Correlation Models

Deterministic Financial Correlation Models

Statistical Correlation Models

Bottom-Up Models

Top-Down Models

– Dynamic conditional correlations (Engle 2002)

– Correlating Brownian motions (Heston 1993)

– Modeling transition rates (Schönbucher 2006)

– Heston model with stochastic correlation (Buraschi et al. 2010)

– Pearson's ρ – Spearman's rank correlation – Kendall's τ

Stochastic Financial Correlation Models

– Binomial correlations (Lucas 1995) – Copulas

– Correlating – Modeling stochastic stochastic volatility and time change (Hurd and Kuznetsov 2006) stochastic correlation (Lu and Meissner 2013) – Contagion default modeling (Giesecke et al. 2009)

One-factor Gaussian copula Multivariate copula (Vasicek 1987) (Li 2000) applied in Basel II – Contagion models (Davis and Lo 2001, and Jarrow and Yu 2001)

FIGURE P.1 Main Statistical and Financial Correlation Models

TARGET AUDIENCE This book should be valuable to anyone who is exposed to financial correlations and financial correlation risk. So it should be of interest to upper management, risk managers, analysts, traders, compliance departments, model validation groups, controllers, reporting groups, brokers, and others. The book contains questions and problems at the end of each chapter, which should facilitate using the book in a classroom. The answers to the problems are available to instructors; please e-mail gunter@dersoft.com.

BASEL III This book addresses new risk measures, especially the new correlation risk measures of the Basel III accord. We discuss the Basel-applied value at risk (VaR) concept, which includes correlated market risk, in the introductory

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Preface

Chapter 1, section 1.3.3. We address the one-factor Gaussian copula (OFGC) correlation model, which underlies the Basel credit correlation framework, in Chapter 6. We revisit the VaR concept for a multi-asset portfolio in Chapter 9, section 9.4. In Chapter 12, we discuss the Basel III correlation framework in detail, deriving credit value at risk (CVaR) and required capital (RC). In particular, we address credit value adjustment (CVA) with general and specific wrong-way risk (WWR), which includes the correlation between general market factors as well as the correlation between specific entities.

ADDITIONAL MATERIALS This book comes with 26 supporting spreadsheets, models, and documents. They can be downloaded at www.wiley.com/go/correlationriskmodeling; password: gunter123. The supporting documents can also be downloaded from the author's website www.dersoft.com/correlationbook/downloads. Below is a breakdown of the supporting documents by file. For a general refresher on the basics of mathematical finance: ■

Math refresher.docx

Chapter 1 ■ ■ ■ ■ ■ ■

2-asset VaR.xlsx Matrix primer.xlsx Exchange option.xls Quanto option.xls Dependence and Correlation.xlsm Log returns.xlsx

Chapter 2 ■

Correlation fitting.docx

Chapter 3 ■

Lookback option.xls

Chapter 4 ■ ■

GBM path with jumps.xlsm 2-asset default time Copula.xlsm

Chapter 5 ■

CDO Gauss educational.xlsm

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PREFACE

Chapter 6 ■ ■

OFGC educational.xls Base correlation generation.xlsm

Chapter 7 ■

Base correlation generation.xlsm

Chapter 8 ■ ■

GBM path with jumps.xlsm Stochastic correlation.xlsx

Chapter 9 ■ ■ ■ ■

VaR educational.xlsm VaR n asset cora gora.xlsm Exchange option cora.docx Math refresher.docx

Chapter 10 ■ ■

CDS with default correlation.xlsm CDS three correlated entities pricing code.docx

Chapter 11 ■ ■ ■ ■

CDS with default correlation.xlsm Option on the better of two.xlsm Correlation swap.xls Interest rate swap pricing model.xls

Chapter 12 ■ ■

CVAR.xlsm Basel double default.xlsm

I welcome feedback. If you have a suggestion or comment, or if you spot an error, please email me at gunter@dersoft.com. There is an errata page at www.dersoft.com/correlationbook/errata.docx.

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CHAPTER

1

Some Correlation Basics: Properties, Motivation, Terminology Behold, the fool saith, “Put not all thine eggs in the one basket.” —Mark Twain

I

n this chapter we introduce the basic concepts of financial correlations and financial correlation risk. We show that correlations are critical in many areas of finance such as investments, trading, and risk management, as well as in financial crises and in financial regulation. We also show how correlation risk relates to other risks in finance such as market risk, credit risk, systemic risk, and concentration risk.

1.1 WHAT ARE FINANCIAL CORRELATIONS? Heuristically (meaning nonmathematically), we can define two types of financial correlations: static and dynamic. Static financial correlations measure how two or more financial assets are associated within a certain time period. Examples are: ■

The classic value at risk (VaR) model. It answers the question: What is the maximum loss of correlated assets in a portfolio with a certain probability for a given time period? This time period can be 10 days as Basel III

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CORRELATION RISK MODELING AND MANAGEMENT

requires, as well as shorter or longer (see Chapter 1, section 1.3.3 and Chapter 9, section 9.4 for more on VaR and correlation). The original copula approach for collateralized debt obligations (CDOs). It measures the default correlations between all assets in the CDO for a certain time period, which is typically identical to the maturity date of the CDO (see Chapter 5 for details). The binomial default correlation model of Lucas (1995), which is a special case of the Pearson correlation model. It measures the probability of two assets defaulting together within a short time period (see Chapter 3 for details). Besides the static correlation concept, there are dynamic correlations:

Dynamic financial correlations measure how two or more financial assets move together in time. Examples are: ■

In practice, pairs trading, a type of statistical arbitrage, is performed. Let’s assume the movements of assets x and y have been highly correlated in time. If now asset x performs poorly with respect to y, then asset x is bought and asset y is sold with the expectation that the gap will narrow. Within the deterministic correlation approaches, the Heston 1993 model correlates the Brownian motions dz1 andffi dz2 of assets 1 and 2. The core pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi equation is dz1 (t) = r dz2 (t) + (1 − p2 ) dz3 (t) where dz1 and dz2 are correlated in time with correlation parameter p. See Chapter 3 for details. Correlations behave randomly and unpredictably. Therefore, it is a good idea to model them as a stochastic process. Stochastic correlation processes are by construction time dependent and can replicate correlation properties well. See Chapter 8 for details.

Suddenly everything was highly correlated. —Financial Times, April 2009

1.2 WHAT IS FINANCIAL CORRELATION RISK? Financial correlation risk is the risk of financial loss due to adverse movements in correlation between two or more variables. These variables can comprise any financial variables. For example, the positive correlation between Mexican bonds and Greek bonds can hurt

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Some Correlation Basics: Properties, Motivation, Terminology

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Mexican bond investors if Greece bond prices decrease, which happened in 2012 during the Greek crisis. Or the negative correlation between commodity prices and interest rates can hurt commodity investors if interest rates rise. A further example is the correlation between a bond issuer and a bond insurer, which can hurt the bond investor (see the example displayed in Figure 1.1). Correlation risk is especially critical in risk management. An increase in the correlation of asset returns increases the risk of financial loss, which is often measured by the value at risk (VaR) concept. For details see section 1.3.3 and Chapter 9, sections 9.4 and 9.5. An increase in correlation is typical in a severe systemic crisis. For example, in the Great Recession from 2007 to 2009, financial assets and financial markets worldwide became highly correlated. Risk managers who had in their portfolios assets with negative or low correlations suddenly witnessed many of them decline together; hence asset correlations increased sharply. For more on systemic risk, see section 1.3.4, “The Global Financial Crisis of 2007 to 2009 and Correlation,” as well as Chapter 2, which displays empirical findings of correlations. Correlation risk can also involve variables that are nonfinancial, such as economic or political events. For example, the correlation between the increasing sovereign debt and currency value can hurt an exporter, as occurred in Europe in 2012, where a decreasing euro hurt U.S. exporters. Geopolitical tensions as, for example, in the Middle East can hurt airline companies due to increasing oil prices, or a slowing gross domestic product (GDP) in the United States can hurt Asian and European exporters and investors, since economies and financial markets are correlated worldwide. Let’s look at correlation risk via an example of a credit default swap (CDS). A CDS is a financial product in which the credit risk is transferred from the investor (or CDS buyer) to a counterparty (CDS seller). Let’s assume an investor has invested $1 million in a bond from Spain. He is now worried about Spain defaulting and has purchased a credit default swap from a French bank, BNP Paribas. Graphically this is displayed in Figure 1.1. The investor is protected against a default from Spain, since in case of default, the counterparty BNP Paribas will pay the originally invested $1 million to the investor. For simplicity, let’s assume the recovery rate and accrued interest are zero. The value of the CDS, i.e., the fixed CDS spread s, is mainly determined by the default probability of the reference entity Spain. However, the spread s is also determined by the joint default correlation of BNP Paribas and Spain. If the correlation between Spain and BNP Paribas increases, the present value of the CDS for the investor will decrease and he will suffer a paper loss. The worst-case scenario is the joint default of Spain and BNP Paribas, in

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CORRELATION RISK MODELING AND MANAGEMENT Fixed CDS spread s Investor and credit default swap buyer i

$1 million

Payout of $1 million in case of default of r

Counterparty c (i.e., credit default swap seller, BNP Paribas)

coupon k

Reference asset of reference entity r (Spain)

FIGURE 1.1 An Investor Hedging His Spanish Bond Exposure with a CDS which case the investor will lose his entire investment in the Spanish bond of $1 million. In other words, the investor is exposed to default correlation risk between the reference asset r (Spain) and the counterparty c (BNP Paribas). Since both Spain and BNP Paribas are in Europe, let’s assume that there is a positive default correlation between the two. In this case, the investor has wrong-way correlation risk or short wrong-way risk (WWR). Let’s assume the default probability of Spain and BNP Paribas both increase. This means that the exposure to the reference entity Spain increases (since the CDS has a higher present value for the investor) and it is more unlikely that the counterparty BNP Paribas can pay the default insurance. We will discuss wrong-way risk, which is a key term in the Basel II and III accords, in Chapter 12. The magnitude of the correlation risk is expressed graphically in Figure 1.2. From Figure 1.2 we observe that for a correlation of −0,3 and higher, the higher the correlation, the lower the CDS spread. This is because an increasing r means a higher probability of the reference asset and the counterparty defaulting together. In the extreme case of a perfect correlation of 1, the CDS is worthless. This is because if Spain defaults, so will the insurance seller BNP Paribas. We also observe from Figure 1.2 that for a correlation from −1 to about −0.3, the CDS spread increases slightly. This seems counterintuitive at first. However, an increase in the negative correlation means a higher probability of either Spain or BNP Paribas defaulting. In the case of Spain defaulting, the CDS buyer will get compensated by BNP Paribas. However, if the insurance seller BNP Paribas defaults, the CDS buyer will lose his

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Some Correlation Basics: Properties, Motivation, Terminology CDS Spread with Respect to Correlation

0.12

CDS Spread

0.1 0.08 0.06 0.04 0.02 0

–1

–0.8

–0.6

–0.4

–0.2 0 0.2 Correlation

0.4

0.6

0.8

1

FIGURE 1.2 CDS Spread s of a Hedged Bond Purchase (as Displayed in Figure 1.1) with Respect to the Default Correlation between the Reference Entity r and the Counterparty c

insurance and will have to repurchase it. This may have to be done at a higher cost. The cost will be higher if the credit quality of Spain has decreased since inception of the original CDS. For example, the CDS spread may have been 3% in the original CDS, but may have increased to 6% due to a credit deterioration of Spain. For more details on pricing CDSs with counterparty risk and the reference asset–counterparty correlation, see Chapter 10, section 10.1, as well as Kettunen and Meissner (2006). We observe from Figure 1.2 that the dependencies between a variable (here the CDS spread) and correlation may be nonmonotonous; that is, the CDS spread sometimes increases and sometimes decreases if correlation increases. We will also encounter this nonmonotony feature of correlation when we discuss the mezzanine tranche of a CDO in Chapter 5.

1.3 MOTIVATION: CORRELATIONS AND CORRELATION RISK ARE EVERYWHERE IN FINANCE Why study financial correlations? That’s an easy one. Financial correlations appear in many areas in finance. We will briefly discuss five areas: (1) investments and correlation, (2) trading and correlation, (3) risk management and correlation, (4) the global financial crisis and correlation, and (5) regulation and correlation. Naturally, if an entity is exposed to correlation, this means that the entity has correlation risk (i.e., the risk of a change in the correlation).

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CORRELATION RISK MODELING AND MANAGEMENT

1.3.1 Investments and Correlation From our studies of the Nobel Prize–winning capital asset pricing model (CAPM) (Markowitz 1952; Sharpe 1964) we remember that an increase in diversification increases the return/risk ratio. Importantly, high diversification is related to low correlation. Let’s show this in an example. Let’s assume we have a portfolio of two assets, X and Y. They have performed as in Table 1.1. Let’s define the return of asset X at time t as xt, and the return of asset Y at time t as yt. A return is calculated as a percentage change, (St − St−1)/St−1, where S is a price or a rate. The average return of asset X for the time frame 2009 to 2013 is mX = 29.03%; for asset Y the average return is mY = 20.07%. If we assign a weight to asset X, wX, and a weight to asset Y, wY, the portfolio return is mP = wX mX + wY mY

(1.1)

where wX + wY = 1. The standard deviation of returns, called volatility, is derived for asset X with equation (1.2): vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n u 1 t (xt −mX )2 sX = n−1 ∑

(1.2)

t=1

where xt is the return of asset X at time t and n is the number of observed points in time. The volatility of asset Y is derived accordingly. Equation 1.2 can be computed with =stdev in Excel and std in MATLAB. From our example in Table 1.1, we find that sX = 44.51% and sY = 47.58%. Let’s now look at the covariance. The covariance measures how two variables covary (i.e., move together). More precisely, the covariance TABLE 1.1 Performance of a Portfolio with Two Assets Year

Asset X

Asset Y

Return of Asset X

Return of Asset Y

2008 2009 2010 2011 2012 2013

100 120 108 190 160 280

200 230 460 410 480 380

20.00% −10.00% 75.93% −15.79% 75.00%

15.00% 100.00% −10.87% 17.07% −20.83%

29.03%

20.07%

Average

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Some Correlation Basics: Properties, Motivation, Terminology

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measures the strength of the linear relationship between two variables. The covariance of returns for assets X and Y is derived with equation (1.3): CovXY =

n 1 (xt − mX )(yt − mY ) n−1 ∑

(1.3)

t=1

For our example in Table 1.1 we derive CovXY = −0.1567. Equation (1.3) is = Covariance.S in Excel and cov in MATLAB. The covariance is not easy to interpret, since it takes values between −∞ and +∞. Therefore, it is more convenient to use the Pearson correlation coefficient rXY, which is a standardized covariance; that is, it takes values between −1 and +1. The Pearson correlation coefficient is: rXY =

CovXY sX sY

(1.4)

For our example in Table 1.1, rXY = −0.7403, showing that the returns of assets X and Y are highly negatively correlated. Equation (1.4) is ‘correl’ in Excel and ‘corrcoef’ in MATLAB. For the derivation of the numerical examples of equations (1.2) to (1.4) and more information on the covariances, see Appendix 1A and the spreadsheet “Matrix primer.xlsx,” sheet “Covariance matrix,” at www.wiley.com/go/correlationriskmodeling under “Chapter 1.” We can calculate the standard deviation for our two-asset portfolio P as sP =

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi w2X s2X + w2Y s2Y + 2wX wY CovXY

(1.5)

With equal weights, i.e., wX = wY = 0.5, the example in Table 1.1 results in sP = 16.66%. Importantly, the standard deviation (or its square, the variance) is interpreted in finance as risk. The higher the standard deviation, the higher the risk of an asset or a portfolio. Is standard deviation a good measure of risk? The answer is: It’s not great, but it’s one of the best we have. A high standard deviation may mean high upside potential, so it penalizes possible profits! But a high standard deviation naturally also means high downside risk. In particular, risk-averse investors will not like a high standard deviation, i.e., high fluctuation of their returns. An informative performance measure of an asset or a portfolio is the risk-adjusted return, i.e., the return/risk ratio. For a portfolio it is mP/sP, which we derived in equations (1.1) and (1.5). In Figure 1.3 we observe one of the few free lunches in finance: the lower (preferably negative) the correlation of the assets in a portfolio, the higher the return/risk ratio. For a rigorous proof, see Markowitz (1952) and Sharpe (1964).

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CORRELATION RISK MODELING AND MANAGEMENT

250%

μP/σP with Respect to Correlation ρ

225% 200% μP/σP

175% 150% 125% 100% 75% 50% 25% 0% –1

–0.5

0 Correlation ρ

0.5

1

FIGURE 1.3 The Negative Relationship of the Portfolio Return/Risk Ratio mP/sP

with Respect to the Correlation r of the Assets in the Portfolio (Input Data are from Table 1.1)

Figure 1.3 shows the high impact of correlation on the portfolio return/ risk ratio. A high negative correlation results in a return/risk ratio of close to 250%, whereas a high positive correlation results in a 50% ratio. The equations (1.1) to (1.5) are derived within the framework of the Pearson correlation approach. We will discuss the limitations of this approach in Chapter 3.

Only by great risks can great results be achieved. —Xeres

1.3.2 Trading and Correlation In finance, every risk is also an opportunity. Therefore, at every major investment bank and hedge fund correlation desks exist. The traders try to forecast changes in correlation and attempt to financially gain from these changes in correlation. We already mentioned the correlation strategy “pairs trading.” Generally, correlation trading means trading assets whose prices are determined at least in part by the comovement of one or more asset in time. Many types of correlation assets exist.

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1.3.2.1 Multi-Asset Options A popular group of correlation options are multi-asset options, also termed rainbow options or mountain range options. Many different types are traded. The most popular ones are listed here. S1 is the price of asset 1 and S2 is the price of asset 2 at option maturity. K is the strike price, i.e., the price determined at option start, at which the underlying asset can be bought in the case of a call, and the price at which the underlying asset can be sold in the case of a put. ■ ■ ■ ■ ■ ■ ■ ■

Option on the better of two. Payoff = max(S1, S2). Option on the worse of two. Payoff = min(S1, S2). Call on the maximum of two. Payoff = max[0, max(S1, S2) − K]. Exchange option (as a convertible bond). Payoff = max(0, S2 − S1). Spread call option. Payoff = max[0, (S2 − S1) − K]. Option on the better of two or cash. Payoff = max(S1, S2, cash). Dual-strike call option. Payoff = max(0, S1 − K1, S2 − K2). h n i Portfolio of basket options. Payoff = ni Si − K; 0 , where ni is the ∑i = 1 weight of assets i.

Importantly, the prices of these correlation options are highly sensitive to the correlation between the asset prices S1 and S2. In the list above, except for the option on the worse of two, the lower the correlation, the higher the option price. This makes sense since a low, preferable negative correlation means that if one asset decreases, on average the other increases. So one of the two assets is likely to result in a high price and a high payoff. Multi-asset options can be conveniently priced using closed form extensions of the BlackScholes-Merton 1973 option model; see Chapter 9 for details. Let’s look at the evaluation of an exchange option with a payoff of max(0, S2 − S1). The payoff shows that the option buyer has the right to give away asset 1 and receive asset 2 at option maturity. Hence, the option buyer will exercise her right if S2 > S1. The price of the exchange option can be derived easily. We first rewrite the payoff equation max(0, S2 − S1) = S1 max[0, (S2/S1) − 1]. We then input the covariance between asset S1 and S2 into the implied volatility function of the exchange option using a variation of equation (1.5): qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (1.5a) sE = s2A + s2B − 2CovAB where sE is the implied volatility of S2/S1, which is input into the standard Black-Scholes-Merton 1973 option pricing model. For an exchange option pricing model and further discussion, see Chapter 9, section 9.2.2 and the model “Exchange option.xls” at www.wiley .com/go/correlationriskmodeling, under “Chapter 1.”

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CORRELATION RISK MODELING AND MANAGEMENT Exchange Option Price with Respect to Correlation

16 14

Price

12 10 8 6 4 2 1

0.9

0.7 0.8

0.6

0.5

0.4

0.3

0.2

0 0.1

–0.1

–0.2

–0.3

–0.4

–0.5

–0.8 –0.7 –0.6

–0.9

–1

0 Correlation

FIGURE 1.4 Exchange Option Price with Respect to Correlation of the Assets in the Portfolio For details on an exchange option as pricing and correlation risk management, see Chapter 9, section 9.2.2.

Importantly, the exchange option price is highly sensitive to the correlation between the asset prices S1 and S2, as seen in Figure 1.4. From Figure 1.4 we observe the strong impact of the correlation on the exchange option price. The price is close to 0 for high correlation and $15.08 for a negative correlation of −1. As in Figures 1.2 and 1.3, the correlation approach underlying Figure 1.4 is the Pearson correlation model. We will discuss the limitations of the Pearson correlation model in Chapter 3. 1.3.2.2 Quanto Option Another interesting correlation option is the quanto option. This is an option that allows a domestic investor to exchange his potential option payoff in a foreign currency back into his home currency at a fixed exchange rate. A quanto option therefore protects an investor against currency risk. For example, an American believes the Nikkei will increase, but she is worried about a decreasing yen, which would reduce or eliminate her profits from the Nikkei call option. The investor can buy a quanto call on the Nikkei, with the yen payoff being converted into dollars at a fixed (usually the spot) exchange rate. Originally, the term quanto comes from the word quantity, meaning that the amount that is reexchanged to the home currency is unknown, because it depends on the future payoff of the option. Therefore the financial institution that sells a quanto call does not know two things: 1. How deep in the money the call will be, i.e., which yen amount has to be converted into dollars.

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Some Correlation Basics: Properties, Motivation, Terminology

2. The exchange rate at option maturity at which the stochastic yen payoff will be converted into dollars. The correlation between (1) and (2) i.e., the price of the underlying Sʹ and the exchange rate X, significantly influences the quanto call option price. Let’s consider a call on the Nikkei Sʹ and an exchange rate X defined as domestic currency per unit of foreign currency (so $/1 yen for a domestic American) at maturity. If the correlation is positive, an increasing Nikkei will also mean an increasing yen. That is favorable for the call seller. She has to settle the payoff, but only needs a small yen amount to achieve the dollar payment. Therefore, the more positive the correlation coefficient, the lower the price for the quanto option. If the correlation coefficient is negative, the opposite applies: If the Nikkei increases, the yen decreases in value. Therefore, more yen are needed to meet the dollar payment. As a consequence, the lower the correlation coefficient, the more expensive the quanto option. Hence we have a similar negative relationship between the option price and correlation as in Figure 1.2. Quanto options can be conveniently priced closed form applying an extension of the Black-Scholes-Merton 1973 model. For a pricing model and a more detailed discussion on a quanto option, see the “Quanto option.xls” model at www.wiley.com/go/correlationriskmodeling under “Chapter 1.” 1.3.2.3 Correlation Swap The correlation between assets can also be traded directly with a correlation swap. In a correlation swap a fixed (i.e., known) correlation is exchanged with the correlation that will actually occur, called realized or stochastic (i.e., unknown) correlation, as seen in Figure 1.5. Paying a fixed rate in a correlation swap is also called buying correlation. This is because the present value of the correlation swap will increase for the correlation buyer if the realized correlation increases. Naturally the fixed rate receiver is selling correlation. The realized correlation r in Figure 1.5 is the correlation between the assets that actually occurs during the time of the swap. It is calculated as: rrealized =

n2

2 r − n ∑ i;j

(1.6)

i>j

Fixed percentage (e.g., ρ = 10%) Correlation fixed rate payer

Realized ρ

Correlation fixed rate receiver

FIGURE 1.5 A Correlation Swap with a Fixed 10% Correlation Rate

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CORRELATION RISK MODELING AND MANAGEMENT

where ri,j is the Pearson correlation between asset i and j, and n is the number of assets in the portfolio. The payoff of a correlation swap for the correlation fixed rate payer at maturity is: N (rrealized − rfixed )

(1.7)

where N is the notional amount. Let’s look at an example of a correlation swap. Correlation swaps can indirectly protect against decreasing stock prices. As we will see in this chapter in section 1.4, as well as in Chapter 2, when stocks decrease, typically the correlation between the stocks increases. Hence a fixed correlation payer protects himself indirectly against a stock market decline.

EXAMPLE 1.1: PAYOFF OF A CORRELATION SWAP What is the payoff of a correlation swap with three assets, a fixed rate of 10%, a notional amount of $1,000,000, and a 1-year maturity? First, the daily log returns ln(St/St−1) of the three assets are calculated for 1 year.1 Let’s assume the realized pairwise correlations of the log returns at maturity are as displayed in Table 1.2. The average correlation between the three assets is derived by equation (1.6). We apply the correlations only in the shaded area from Table 1.2, since these satisfy i > j. Hence we have rrealized = 32 2 3 − (0:5 + 0:3 + 0:1) = 0:3. Following equation (1.7), the payoff for the correlation fixed rate payer at swap maturity is $1,000,000 ´ (0.3 − 0.1) = $200,000. TABLE 1.2

Si=1 Si=2 Si=3

Pairwise Pearson Correlation Coefficient at Swap Maturity Sj=1

Sj=2

Sj=3

1 0.5 0.1

0.5 1 0.3

0.1 0.3 1

1. Log returns ln(S1/S0) are an approximation of percentage returns (S1 − S0)/S0. We typically use log returns in finance since they are additive in time, whereas percentage returns are not. For details see Appendix 1B.

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Currently, year 2013, there is no industry-standard valuation model for correlation swaps. Traders often use historical data to anticipate rrealized. In order to apply swap valuation techniques, we require a term structure of correlation in time. However, no correlation term structure currently exists. We can also apply stochastic correlation models to value a correlation swap. Stochastic correlation models are currently emerging and will be discussed in Chapter 8. 1.3.2.4 Buying Call Options on an Index and Selling Call Options on Individual Components Another way of buying correlation (i.e., benefiting from an increase in correlation) is to buy call options on an index such as the Dow Jones Industrial Average (the Dow) and sell call options on individual stocks of the Dow. As we will see in Chapter 2, there is a positive relationship between correlation and volatility. Therefore, if correlation between the stocks of the Dow increases, so will the implied volatility2 of the call on the Dow. This increase is expected to outperform the potential loss from the increase in the short call positions on the individual stocks. Creating exposure on an index and hedging with exposure on individual components is exactly what the “London whale,” JPMorgan’s London trader Bruno Iksil, did in 2012. Iksil was called the London whale because of his enormous positions in credit default swaps (CDSs).3 He had sold CDSs on an index of bonds, the CDX.NA.IG.9, and hedged them by buying CDSs on individual bonds. In a recovering economy this is a promising trade: Volatility and correlation typically decrease in a recovering economy. Therefore, the sold CDSs on the index should outperform (decrease more than) the losses on the CDSs of the individual bonds. But what can be a good trade in the medium and long term can be disastrous in the short term. The positions of the London whale were so large that hedge funds short-squeezed him: They started to aggressively buy the CDS index CDX.NA.IG.9. This increased the CDS values in the index and created a huge (paper) loss for the whale. JPMorgan was forced to buy back the CDS index positions at a loss of over $2 billion.

2. Implied volatility is volatility derived (implied) by option prices. The higher the implied volatility, the higher the option price. 3. Simply put, a credit default swap (CDS) is an insurance against default of an underlying (e.g., a bond). However, if the underlying is not owned, a long CDS is a speculative instrument on the default of the underlying (just like a naked put on a stock is a speculative position on the stock going down). See Meissner (2005) for more.

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CORRELATION RISK MODELING AND MANAGEMENT

1.3.2.5 Paying Fixed in a Variance Swap on an Index and Receiving Fixed on Individual Components A further way to buy correlation is to pay fixed in a variance swap on an index and to receive fixed in variance swaps on individual components of the index. The idea is the same as the idea with respect to buying a call on an index and selling a call on the individual components: If correlation increases, so will the variance. As a consequence, the present value for the variance swap buyer, the fixed variance swap payer, will increase. This increase is expected to outperform the potential losses from the short variance swap positions on the individual components. In the preceding trading strategies, the correlation between the assets was assessed with the Pearson correlation approach. As mentioned, we will discuss the limitations of this model in Chapter 3.

1.3.3 Risk Management and Correlation After the global financial crisis from 2007 to 2009, financial markets have become more risk averse. Commercial banks, investment banks, as well as nonfinancial institutions have increased their risk management efforts. As in the investment and trading environment, correlation plays a vital part in risk management. Let’s first clarify what risk management means in finance. Financial risk management is the process of identifying, quantifying, and, if desired, reducing financial risk. The three main types of financial risk are: 1. Market risk. 2. Credit risk. 3. Operational risk. Additional types of risk may include systemic risk, liquidity risk, volatility risk, and the topic of this book, correlation risk. We will concentrate in this introductory chapter on market risk. Market risk consists of four types of risk: (1) equity risk, (2) interest rate risk, (3) currency risk, and (4) commodity risk. There are several concepts to measure the market risk of a portfolio, such as value at risk (VaR), expected shortfall (ES), enterprise risk management (ERM), and more. VaR is currently (year 2013) the most widely applied risk management measure. Let’s show the impact of asset correlation on VaR.4 First, what is value at risk (VaR)? VaR measures the maximum loss of a portfolio with respect to a certain probability for a certain time frame. The equation for VaR is: pffiffiffi (1.8) VaRP = sP a x 4. We use a variance-covariance VaR approach in this book to derive VaR. Another way to derive VaR is the nonparametric VaR. This approach derives VaR from simulated historical data. See Markovich (2007) for details.

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Some Correlation Basics: Properties, Motivation, Terminology

where VaRP is the value at risk for portfolio P, and a is the abscise value of a standard normal distribution corresponding to a certain confidence level. It can be derived as =normsinv(confidence level) in Excel or norminv (confidence level) in MATLAB. a takes the values −∞ < a <+∞. x is the time horizon for the VaR, typically measured in days; sP is the volatility of the portfolio P, which includes the correlation between the assets in the portfolio. We calculate sP via sP =

pffiffiffiffiffiffiffiffiffiffiffiffiffi bh Cbv

(1.9)

where bh is the horizontal b vector of invested amounts (price time quantity), bv is the vertical b vector of invested amounts (also price time quantity),5 and C is the covariance matrix of the returns of the assets. Let’s calculate VaR for a two-asset portfolio and then analyze the impact of different correlations between the two assets on VaR.

EXAMPLE 1.2: DERIVING VaR OF A TWO-ASSET PORTFOLIO What is the 10-day VaR for a two-asset portfolio with a correlation coefficient of 0.7, daily standard deviation of returns of asset 1 of 2%, of asset 2 of 1%, and $10 million invested in asset 1 and $5 million invested in asset 2, on a 99% confidence level? First, we derive the covariances (Cov): Cov11 = r11 s1 s1 = 1 ´ 0:02 ´ 0:02 = 0:00046 Cov12 = r12 s1 s2 = 0:7 ´ 0:02 ´ 0:01 = 0:00014 Cov21 = r21 s2 s1 = 0:7 ´ 0:01 ´ 0:02 = 0:00014 Cov22 = r22 s2 s2 = 1 ´ 0:01 ´ 0:01 = 0:0001

(1.10) (continued)

5. More mathematically, the vector bh is the transpose of the vector bv, and vice versa: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi T T bh = bv and bv = bh. Hence we can also write equation (1.9) as sP = bh Cbh T . See the spreadsheet “Matrix primer.xls,” sheet “Matrix Transpose,” at www.wiley.com/ go/correlationriskmodeling, under “Chapter 1.” 6. The attentive reader realizes that we calculated the covariance differently in equation (1.3). In equation (1.3) we derived the covariance from scratch, inputting the return values and means. In equation (1.10) we are assuming that we already know the correlation coefficient r and the standard deviation s.

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CORRELATION RISK MODELING AND MANAGEMENT

� (continued) 0:0004 Hence our covariance matrix is C = 0:00014

0:00014 0:0001

Let’s calculate sP following equation (1.9). We first derive bhC � � 0:0004 0:00014 (10 5) = (10 ´ 0:0004 + 5 ´ 0:00014 10 ´ 0:00014 0:00014 0:0001 + 5 ´ 0:0001) = (0:0047 0:0019) � � 10 = 10 ´ 0:0047 + 5 ´ 0:0019 and then (bh C)bv = (0:0047 0:0019) 5 7 = 5:65%pffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffi Hence we have sP = bh Cbv = 5:65% = 23:77%. We find the value for a in equation (1.8) from Excel as = normsinv (0.99) = 2.3264, or from MATLAB as norminv(0.99) = 2.3264. Following pffiffiffiffiffiffiequation (1.8), we now calculate the VaRP as 0.2377 ´ 2.3264 ´ 10 = 1.7486. Interpretation: We are 99% certain that we will not lose more than $1.75486 million in the next 10 days due to market price changes of asset 1 and 2.

The number $1.7486 million is the 10-day VaR on a 99% confidence level. This means that on average once in a hundred 10-day periods (so once every 1,000 days) this VaR number of $1.7486 million will be exceeded. If we have roughly 250 trading days in a year, the company is expected to exceed the VaR about once every four years. The Basel Committee for Banking Supervision (BCBS) considers this to be too often. Hence, it requires banks, which are allowed to use their own models (called internal model-based approach), to hold capital for assets in the trading book8 in the amount of at least 3 times the 10-day VaR (plus a specific risk charge for credit risk). In example 1.2, if a bank is granted the minimum of 3 times the VaR, a VaR

7. The spreadsheet “2-asset VaR.xlsx,” which derives the values in example 1.2, can be found at www.wiley.com/go/correlationriskmodeling, section under “Chapter 1.” 8. Assets that are marked-to-market, such as stocks, futures, options, and swaps, are in the trading book. Some assets, such as loans and certain bonds, which are not marked-to-market, are in the banking book.

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Some Correlation Basics: Properties, Motivation, Terminology VaR with Respect to Correlation

1.9 1.8 1.7

VaR

1.6 1.5 1.4 1.3 1.2 1.1 1

–1

–0.8

–0.6

–0.4

–0.2 0 0.2 Correlation

0.4

0.6

0.8

1

FIGURE 1.6 VaR of the Two-Asset Portfolio of Example 1.2 with Respect to Correlation r between Asset 1 and Asset 2

capital charge for assets in the trading book of $1,7486 million ´ 3 = $5.2539 million is required by the Basel Committee.9 Let’s now analyze the impact of different correlations between the asset 1 and asset 2 on VaR. Figure 1.6 shows the impact. As expected, we observe from Figure 1.6 that the lower the correlation, the lower the risk, measured by VaR. Preferably the correlation is negative. In this case, if one asset decreases, the other asset on average increases, hence reducing the overall risk. The impact of correlation on VaR is strong. For a perfect negative correlation of −1, VaR is $1.1 million; for a perfect positive correlation, VaR is close to $1.9 million.

9. In a recent Consultative Document (May 2012), the Basel Committee has indicated that it is considering replacing VaR with expected shortfall (ES). Expected shortfall measures tail risk (i.e., the size and probability of losses beyond a certain threshold). See www.bis.org/publ/bcbs219.pdf for details. Loosely speaking, VaR answers the question: “What is the maximum loss in good times?” Expected shortfall answers the question: “What is the loss in bad times?”

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CORRELATION RISK MODELING AND MANAGEMENT

There are no toxic assets, just toxic people.

1.3.4 The Global Financial Crisis of 2007 to 2009 and Correlation Currently, in 2013, the global financial crisis of 2007 to 2009 seems almost like a distant memory. The U.S. stock market has recovered from its low in March 2009 of 6,547 points and has more than doubled to over 15,000. World economic growth is at a moderate 2.5%. However, the U.S. unemployment rate is stubbornly high at around 8% and has not decreased to pre-crisis levels of about 5%. Most important, to fight the crisis, countries engaged in huge stimulus packages to revive their faltering economies. As a result, enormous sovereign deficits are plaguing the world economy. The European debt crisis, with Greece, Cyprus, and other European nations virtually in default, is a major global economic threat. The U.S. debt is also far from benign with a debt-to-GDP ratio of over 80%. One of the few nations that is enjoying these enormous debt levels is China, which is happy buying the debt and taking in the proceeds. A crisis that brought the financial and economic system worldwide to a standstill is naturally not monocausal, but has many reasons. Here are the main ones: ■

An extremely benign economic and risk environment from 2003 to 2006 with record low credit spreads, low volatility, and low interest rates. Increasing risk taking and speculation of traders and investors who tried to benefit in these presumably calm times. This led to a bubble in virtually every market segment, such as the housing market, mortgage market (especially the subprime mortgage market), stock market, and commodity market. In 2007, U.S. investors had borrowed 470% of the U.S. national income to invest and speculate in the real estate, financial, and commodity markets. A new class of structured investment products, such as collateralized debt obligations (CDOs), CDO-squareds, constant-proportion debt obligations (CPDOs), constant-proportion portfolio insurance (CPPI), as well as new products like options on credit default swaps (CDSs), credit indexes, and the like. The new copula correlation model, which was trusted naively by many investors and which could presumably correlate the n(n − 1)/2 assets in a structured product. Most CDOs contained 125 assets. Hence there

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Some Correlation Basics: Properties, Motivation, Terminology

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are 125(125 − 1)/2 = 7,750 asset correlation pairs to be quantified and managed. For details see Chapters 5 and 6. A moral hazard of rating agencies, which were paid by the same companies whose assets they rated. As a consequence, many structured products received AAA ratings and gave the illusion of little price and default risk. Risk managers and regulators who lowered their standards in light of the greed and profit frenzy. We recommend an excellent (anonymous) paper in the Economist: “A Personal View of the Crisis, Confessions of a Risk Manager.”

The topic of this book is correlation risk, so let’s concentrate on the correlation aspect of the crisis. Around 2003, two years after the Internet bubble burst, the risk appetite of the financial markets increased, and investment banks, hedge funds, and private investors began to speculate and invest in the stock markets, commodity markets, and especially the real estate market. In particular, residential mortgages became an investment object. The mortgages were packaged in collateralized debt obligations (CDOs; see Chapter 5 for a detailed discussion), and then sold off to investors nationally and internationally. The CDOs typically consist of several tranches; that is, the investor can choose a particular degree of default risk. The equity tranche holder is exposed to the first 3% of mortgage defaults, the mezzanine tranche holder is exposed to the 3% to 7% of defaults, and so on. The new copula correlation model derived by Abe Sklar in 1959 and transferred to finance by David Li in 2000 could presumably manage the default correlations in the CDOs (see Chapters 5 and 6 for details). The first correlation-related crisis, which was a forerunner of the major one to come in 2007 to 2009, occurred in May 2005. General Motors was downgraded to BB and Ford was downgraded to BB+, so both companies were now in junk status. A downgrade to junk status typically leads to a sharp bond price decline, since many mutual funds and pension funds are not allowed to hold junk bonds. Importantly, the correlation of the bonds in CDOs that referenced investment grade bonds decreased, since bonds of different credit qualities are typically lower correlated. This led to huge losses of hedge funds, which had put on a strategy where they were short the equity tranche of the CDO and long the mezzanine tranche of the CDO. Figure 1.7 shows the dilemma. Hedge funds had shorted the equity tranche10 (0% to 3% in Figure 1.7) to

10. Shorting the equity tranche means being short credit protection or selling credit protection, which means receiving the (high) equity tranche contract spread.

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CORRELATION RISK MODELING AND MANAGEMENT

FIGURE 1.7 CDO Tranche Spread with Respect to Correlation collect the high equity tranche spread. They had then presumably hedged11 the risk by going long the mezzanine tranche12 (3% to 7% in Figure 1.7). However, as we can see from Figure 1.7, this hedge is flawed. When the correlations of the assets in the CDO decreased, the hedge funds lost on both positions. 1. The equity tranche spread increased sharply; see arrow 1. Hence the fixed spread that the hedge funds received in the original transaction was now significantly lower than the current market spread, resulting in a paper loss. 2. In addition, the hedge funds lost on their long mezzanine tranche positions, since a lower correlation lowers the mezzanine tranche spread; see arrow 2. Hence the spread that the hedge funds paid in the original transactions was now higher than the market spread, resulting in another paper loss. As a result of the huge losses, several hedge funds, such as Marin Capital, Aman Capital, and Baily Coates Cromwell, filed for bankruptcy. It is important to point out that the losses resulted from a lack of understanding of the correlation properties of the tranches in the CDO. The CDOs 11. To hedge means to protect or to reduce risk. See Chapter 11, section 11.1, for details. 12. Going long the mezzanine tranche means being long credit protection or buying credit protection, which means paying the (fairly low) mezzanine tranche contract spread.

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Some Correlation Basics: Properties, Motivation, Terminology

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themselves can hardly be blamed or be called toxic for their correlation properties. From 2003 to 2006 the CDO market, mainly referencing residential mortgages, had exploded, increasing from $64 billion to $455 billion. To fuel the CDOs, more and more questionable subprime mortgages were given, named NINJA loans, standing for “no income, no job or assets.” When housing prices started leveling off in 2006, the first mortgages started to default. In 2007 more and more mortgages defaulted, finally leading to a real estate market collapse. With it the huge CDO market collapsed, leading to the stock market and commodity market crash and a freeze in the credit markets. The financial crisis spread to the world economies, creating a global severe recession, now called the Great Recession. In a systemic crash like this, naturally many types of correlations increase (see also Figure 1.8). From 2007 to 2009, default correlations of the mortgages in the CDOs increased. This actually helped equity tranche investors, as we can see from Figure 1.7: If default correlations increase, the equity tranche spread decreases, leading to an increase in the value of the equity tranche. However, this increase was overcompensated by a strong increase in default probability of the mortgages. As a consequence, tranche spreads increased sharply, resulting in huge losses for the equity tranche investors as well as investors in the other tranches. Correlations between the tranches of the CDOs also increased during the crisis. This had a devastating effect on the super-senior tranches. In normal times, these tranches were considered extremely safe since (1) they were AAA rated and (2) they were protected by the lower tranches. But with the increased tranche correlation and the generally deteriorating credit market, these super-senior tranches were suddenly considered risky and lost up to 20% of their value. To make things worse, many investors had leveraged the super-senior tranches, termed leveraged super-senior (LSS) tranches, to receive a higher spread. This leverage was typically 10 to 20 times, meaning an investor paid $10,000,000 but had risk exposure of $100,000,000 to $200,000,000. What made things technically even worse was that these LSSs came with an option for the investors to unwind the super-senior tranche if the spread had widened (increased). Many investors started to purchase the LSS spread at very high levels, realizing a loss and increasing the LSS tranche spread even further. In addition to the overinvestment in CDOs, the credit default swap (CDS) market also exploded from its beginnings in the mid-1990s from about $8 trillion in 2004 to almost $60 trillion in 2007. CDSs are typically used as insurance to protect against default of a debtor, as we explained in Figure 1.1. No one will argue that an insurance contract is toxic. On the contrary, it is the

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CORRELATION RISK MODELING AND MANAGEMENT

principle of an insurance contract to spread the risk to a wider audience and hence reduce individual risk, as we can see from health insurance or life insurance contracts. CDSs, though, can also be used as speculative instruments. For example, the CDS seller (i.e., the insurance seller) hopes that the insured event (e.g., default of a company or credit deterioration of the company) will not occur. In this case the CDS seller keeps the CDS spread (i.e., the insurance premium) as income, as American International Group (AIG) tried to do in the crisis. A CDS buyer who does not own the underlying asset is speculating on the credit deterioration of the underlying asset, just like a naked put option holder speculates on the decline of the underlying asset. So who is to blame for the 2007–2009 global financial crisis? The quants, who created the new products such as CDSs and CDOs and the models to value them? The upper management and the traders, who authorized and conducted the overinvesting and extreme risk taking? The rating agencies, who gave an AAA rating to many of the CDOs? The regulators, who approved the overinvestments? The risk managers, who allowed the excessive risk taking? The entire global financial crisis can be summed up in one word: Greed! It was the upper management, the traders, and the investors who engaged in excessive trading and irresponsible risk taking to receive high returns, huge salaries, and generous bonuses. For example, the London unit of AIG had sold close to $500 billion in CDSs without much reinsurance! Their main hedging strategy seemed to have been: Pray that the insured contracts don’t deteriorate. The investment banks of the small Northern European country of Iceland had borrowed 10 times Iceland’s national GDP and invested it. With this leverage, Iceland naturally went de facto into bankruptcy in 2008, when the credit markets deteriorated. Lehman Brothers, before filing for bankruptcy in September 2008, reported a leverage of 30.7 (i.e., $691 billion in assets and only $22 billion in stockholders’ equity). The true leverage was even higher, since Lehman tried to hide the leverage with materially misleading repo transactions.13 In addition, Lehman had 1.5 million derivatives transactions with 8,000 different counterparties on its books. Did the upper management and traders of hedge funds and investment banks admit to their irresponsible leverage, excessive trading, and risk taking? No. Instead they created the myth of the toxic asset, which is absurd.

13. Repo stands for repurchase transaction. It can be viewed as a short-term collateralized loan.

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It is like a murderer saying, “I did not shoot that person. It was my gun!” Toxic are not the financial products, but humans and their greed. Most traders were well aware of the risks that they were taking. In the few cases where traders did not understand the risks, the asset itself cannot be blamed; rather, the incompetence of the trader is the reason for the loss. While it is ethically disappointing that the investors and traders did not admit to their wrongdoing, at the same time it is understandable. If they admitted to irresponsible trading and risk taking, they would immediately be prosecuted. Naturally, risk managers and regulators have to take part of the blame for allowing the irresponsible risk taking. The moral hazard of the rating agencies, being paid by the same companies whose assets they rate, also needs to be addressed. We will discuss the role of financial models, their benefits, and their limitations at the beginning of Chapter 3.

1.3.5 Regulation and Correlation Correlations are critical inputs in regulatory frameworks such as the Basel accords, especially in regulations for market risk and credit risk. We will discuss the correlation approaches of the Basel accords in this book. First, let’s clarify. 1.3.5.1 What Are Basel I, II, and III? Basel I, implemented in 1988; Basel II, implemented in 2006; and Basel III, which is currently being developed and implemented until 2018, are regulatory guidelines to ensure the stability of the banking system. The term Basel comes from the beautiful city of Basel in Switzerland, where the honorable regulators meet. None of the Basel accords has legal authority. However, most countries (about 100 for Basel II) have created legislation to enforce the Basel accords for their banks. 1.3.5.2 Why Basel I, II, and III? The objective of the Basel accords is to “provide incentives for banks to enhance their risk measurement and management systems” and “to contribute to a higher level of safety and soundness in the banking system.” In particular, Basel III is being developed to address the deficiencies of the banking system during the financial crisis of 2007 to 2009. Basel III introduces many new ratios to ensure liquidity and adequate leverage of banks. In addition, new correlation models will be implemented that deal with double defaults in insured risk transactions as displayed in Figure 1.1. Correlated defaults in a multi-asset portfolio quantified with the Gaussian copula, correlations in derivatives transactions termed credit value adjustment (CVA), and correlations in what is

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CORRELATION RISK MODELING AND MANAGEMENT

called wrong-way risk (WWR) are currently being discussed. We devote the entire Chapter 12 to addressing the benefits and limitations of these correlation approaches in Basel III.

1.4 HOW DOES CORRELATION RISK FIT INTO THE BROADER PICTURE OF RISKS IN FINANCE? As already mentioned in section 1.3.3, we differentiate three main types of risks in finance: 1. Market risk 2. Credit risk 3. Operational risk Additional types of risk may include systemic risk, concentration risk, liquidity risk, volatility risk, legal risk, reputational risk, and more. Correlation risk plays an important part in market risk and credit risk, and is closely related to systemic risk and concentration risk. Let’s discuss it.

1.4.1 Correlation Risk and Market Risk Correlation risk is an integral part of market risk. Market risk, comprised of equity risk, interest rate risk, currency risk, and commodity risk. Market risk is typically measured with the value at risk (VaR) concept. VaR has a covariance matrix of the assets in the portfolio as an input. So market risk implicitly incorporates correlation risk, i.e., the risk that the correlations in the covariance matrix change. We have already studied the impact of different correlations on VaR in section 1.3.3, “Risk Management and Correlation.” Market risk is also quantified with expected shortfall (ES), also termed conditional VaR or tail risk. Expected shortfall measures market risk for extreme events, typically for the worst 0.1%, 1%, or 5% of possible future scenarios. A rigorous valuation of expected shortfall naturally includes the correlation between the asset returns in the portfolio, as VaR does.14

14. Unfortunately, different authors use different definitions (and notation) for ES. To study ES, we recommend the original ES paper by Artzner et al. (1997), an educational paper by Yamai and Yoshiba (2002), as well as Acerbi and Tasche (2001) and McNeil, Frey, and Embrechts (2005).

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1.4.2 Correlation Risk and Credit Risk Correlation risk is also a critical part of credit risk. Credit risk is comprised of (1) migration risk and (2) default risk. Migration risk is the risk that the credit quality of a debtor decreases, i.e., migrates to a lower credit state. A lower credit state typically results in a lower asset price, so a paper loss for the creditor. We already studied in section 1.2 the effect of correlation risk of an investor who has hedged his bond exposure with a CDS. We derived that the investor is exposed to the correlation between the reference asset and the counterparty, the CDS seller. The higher the correlation, the higher the CDS paper loss for the investor and, importantly, the higher the probability of a total loss of the investment. The degree to which defaults occur together (i.e., default correlation) is critical for financial lenders such as commercial banks, credit unions, mortgage lenders, and trusts, which give many types of loans to companies and individuals. Default correlations are also critical for insurance companies, which are exposed to credit risk of numerous debtors. Naturally, a low default correlation of debtors is desired to diversify the credit risk. Table 1.3 shows the default correlation from 1981 to 2001 of 6,907 companies, of which 674 defaulted. The default correlations in Table 1.3 are one-year default correlations averaged over the time period 1981 to 2001. We will see how to calculate default correlations in Chapter 4, especially in section 4.2, “The Binomial Correlation Measure” (Lucas 1995). From Table 1.3, we observe that default correlations between industries are mostly positive with the exception of the energy sector. This sector is typically viewed as a recession-resistant, stable sector with little or no correlation to other sectors. We also observe that the default correlation within sectors is higher than between sectors. This suggests that systematic factors (e.g., a recession or structural weakness such as the general decline of a sector) have a greater impact on defaults than do idiosyncratic factors. Hence if General Motors defaults, it is more likely that Ford will default, rather than Ford benefiting from the default of its rival. Since the intrasector default correlations are higher than intersector default correlations, a lender is advised to have a sector-diversified loan portfolio to reduce default correlation risk. Defaults are binomial events, either default or no default. So principally we can use a simple correlation model such as the binomial model of Lucas (1995) to analyze them, which we will do in Chapter 4, section 4.2. However, we can also analyze defaults in more detail and look at term structure of defaults. Let’s assume a creditor has given loans to two debtors. One debtor is

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Cons

Ener

Fin

Build

Chem

HiTech

Insur

Leis

Tele

Trans

Util

Source: Standard & Poor’s (S&P) 500.

Correlations above 5% are bold.

Auto 3.80% 1.30% 1.20% 0.40% 1.10% 1.60% 2.80% −0.50% 1.00% 3.90% 1.30% 0.50% Cons 1.30% 2.80% −1.40% 1.20% 2.80% 1.60% 1.80% 1.10% 1.30% 3.20% 1.30% 1.90% Ener 1.20% −1.40% 6.40% −2.50% −0.50% 0.40% −0.10% −1.60% −1.00% −1.40% −0.10% 0.70% Fin 0.40% 1.20% −2.50% 5.20% 2.60% 0.10% 2.30% 3.00% 1.60% 3.70% 1.50% 4.50% Build 1.10% 2.80% −0.50% 2.60% 6.10% 1.20% 2.30% 1.80% 2.30% 6.50% 4.20% 1.30% Chem 1.60% 1.60% 0.40% 0.10% 1.20% 3.20% 1.40% −1.10% 1.10% 2.80% 1.10% 1.00% HiTech 2.80% 1.80% −0.10% 0.40% 2.30% 1.40% 3.30% 0.00% 1.10% 2.80% 1.10% 1.00% Insur −0.50% 1.10% −1.60% 3.00% 1.80% −1.10% 0.00% 5.60% 1.20% −2.60% 2.30% 1.40% Leis 1.00% 1.30% −1.00% 1.60% 2.30% 1.10% 1.40% 1.20% 2.30% 4.00% 2.30% 0.60% Tele 3.90% 3.20% −1.40% 3.70% 6.50% 2.80% 4.70% −2.60% 4.00% 10.70% 3.20% −0.80% Trans 1.30% 2.70% −0.10% 1.50% 4.20% 1.10% 1.90% 2.30% 2.30% 3.20% 4.30% −0.20% Util 0.50% 1.90% 0.70% 4.50% 1.30% 1.00% 1.00% 1.40% 0.60% −0.80% −0.20% 9.40%

Auto

One-Year U.S. Default Correlations—Non-Investment-Grade Bonds 1981–2001

TABLE 1.3 Default Correlation of 674 Defaulted Companies by Industry


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Some Correlation Basics: Properties, Motivation, Terminology

TABLE 1.4

Term Structure of Default Probabilities for an A-Rated Bond and a CC-Rated Bond in 2002 Year 1

2

3

4

5

6

7

8

9

10

A 0.02% 0.07% 0.13% 0.14% 0.15% 0.17% 0.18% 0.21% 0.24% 0.25% CC 23.83% 13.29% 10.31% 7.62% 5.04% 5.13% 4.04% 4.62% 2.62% 2.04%

Source: Moody’s.

A rated, and one is CC rated. A historical default term structure these bonds is displayed in Table 1.4. For most investment grade bonds, the term structure of default probabilities increases in time, as we see from Table 1.4 for the A-rated bond. This is because the longer the time horizon, the higher the probability of adverse internal events such as mismanagement, or adverse external events such as increased competition or a recession. For bonds in distress, however, the default term structure is typically inverse, as seen for the CC-rated bond in Table 1.4. This is because for a distressed company the immediate future is critical. If the company survives the coming problematic years, the probability of default decreases. For a creditor, the default correlation of her debtors is critical. As mentioned, a creditor will benefit from a low default correlation of her debtors, which spreads the default correlation risk. We can correlate the default term structures in Table 1.4 with the famous (now infamous) copula model, which will be discussed in Chapter 4. This will allow us to answer such questions as: “What is the joint probability of debtor 1 defaulting in year 3 and debtor 2 defaulting in year 5?”

Correlations always increase in stressed markets. —John Hull

1.4.3 Correlation Risk and Systemic Risk So far, we have analyzed correlation risk with respect to market risk and credit risk and have concluded that correlations are a critical input when quantifying market risk and credit risk. Correlations are also closely related to systemic risk, which we define here.

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CORRELATION RISK MODELING AND MANAGEMENT

SYSTEMIC RISK The risk of a financial market or an entire financial system collapsing.

An example of systemic risk is the collapse of the entire credit market in 2008. At the height of the crisis in September 2008, when Lehman Brothers filed for bankruptcy, the credit markets were virtually frozen with essentially no lending activities. Even as the Federal Reserve guaranteed interbank loans, lending resumed only very gradually and slowly. The stock market crash starting in October 2007 with the Dow Jones Industrial Average at 14,093 points and then falling by 53.54% to 6,547 points by March 2009 is also a systemic market collapse. All but one of the Dow 30 stocks had declined. Walmart was the lone Dow stock that was up during the crisis. Of the S&P 500 stocks, 489 declined during this time frame. The 11 stocks that were up were: 1. Apollo Group (APOL), educational sector; provides educational programs for working adults and is a subsidiary of the University of Phoenix. 2. AutoZone (AZO), auto industry; provides auto replacement parts. 3. CF Industries (CF), agricultural industry; provides fertilizer. 4. DeVry Inc. (DV), educational sector; holding company of several universities. 5. Edward Lifesciences (EW), pharmaceutical industry; provides products to treat cardiovascular diseases. 6. Family Dollar (FDO), consumer staples. 7. Gilead Pharmaceuticals (GILD), pharmaceutical industry; provides HIV, hepatitis medications. 8. Netflix (NFLX), entertainment industry; provides Internet subscription service. 9. Ross Stores (ROST), consumer staples. 10. Southwestern Energy (SWN), energy sector. 11. Walmart (WMT), consumer staples. From this list we can see that the consumer staples sector (which provides such basic necessities as food and household items) fared well during the crisis. The educational sector also typically thrives in a crisis, since many unemployed seek to further their education. Importantly, systemic financial failures such as the one from 2007 to 2009 typically spread to the economy, with a decreasing GDP, increasing unemployment, and therefore a decrease in the standard of living.

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Some Correlation Basics: Properties, Motivation, Terminology Dow and Correlation of Stocks in the Dow 14,000

60%

13,000

50%

12,000 11,000

40%

10,000

30%

9,000 8,000

20%

7,000

10%

6,000 5,000 2/1/2009

11/1/2008

8/1/2008

5/1/2008

2/1/2008

11/1/2007

8/1/2007

5/1/2007

2/1/2007

11/1/2006

8/1/2006

5/1/2006

2/1/2006

11/1/2005

8/1/2005

5/1/2005

2/1/2005

11/1/2004

8/1/2004

5/1/2004

0%

FIGURE 1.8 Relationship between the Dow (graph with triangles, numerical values on left axis) and Correlation between the Stocks in the Dow (numerical values on right axis)

Systemic risk and correlation risk are highly dependent. Since a systemic decline in stocks involves almost the entire stock market, correlations between the stocks increase sharply. Figure 1.8 shows the relationship between the percentage change of the Dow Jones Industrial Average, short “Dow,” and the correlation between the stocks in the Dow before the crisis from May 2004 to October 2007 and during the crisis from October 2007 to March 2009. In Figure 1.8 we downloaded daily closing prices of all 30 stocks in the Dow and put them into monthly bins. We then derived monthly 30 ´ 30 correlation matrices using the Pearson correlation measure and averaged the matrices. We then smoothed the graph by taking the one-year moving average. From Figure 1.8 we can observe a somewhat stable correlation from 2004 to 2006, when the Dow increased moderately. In the time period from January 2007 to February 2008 we observe that the correlation in the Dow increases when the Dow increases more strongly. Importantly, in the time of the severe decline of the Dow from August 2008 to March 2009, we observe a sharp increase in the correlation from noncrisis levels of on average 27% to over 50%. In Chapter 2, we will observe empirical correlations in detail, and we will find that at the height of the crisis in February 2009 the correlation of the stocks in the Dow reached a high of 96.97%. Hence, portfolios that were considered well diversified in benign times experienced a sharp increase in correlation and hence unexpected losses due to the combined, highly correlated decline of many

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CORRELATION RISK MODELING AND MANAGEMENT

stocks during the crisis. We will quantify this correlation risk and its associated potential losses in detail in Chapters 9 and 10.

1.4.4 Correlation Risk and Concentration Risk Concentration risk is a fairly new risk category and therefore not yet uniquely defined. We provide a sensible definition.

CONCENTRATION RISK The risk of financial loss due to a concentrated exposure to a particular group of counterparties.

Concentration risk can be quantified with the concentration ratio. For example, if a creditor has 10 loans of equal size, the concentration ratio would be 1/10 = 0.1. If a creditor has only one loan to one counterparty, the concentration ratio would be 1. Naturally, the lower the concentration ratio, the more diversified is the default risk of the creditor, assuming the default correlation between the counterparties is smaller than 1. We can also categorize counterparties into groups, for example sectors. We can then analyze sector concentration risk. The higher the number of different sectors a creditor has lent to, the higher is the sector diversification. High sector diversification reduces default risk, since intrasector defaults are more highly correlated than counterparties in different sectors, as seen in Table 1.3. Naturally, concentration risk and correlation risk are closely related. Let’s verify this in an example.

EXAMPLE 1.3: CONCENTRATION RATIO AND CORRELATION CASE A The commercial bank C has lent $10,000,000 to a single company, W. So C’s concentration ratio is 1. Let’s assume company W has a default probability (PW) of 10%. Hence the expected loss (EL) for bank C is $10,000,000 ´ 0.1 = $1,000,000 (see Figure 1.9).

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Some Correlation Basics: Properties, Motivation, Terminology

PW = 10%

FIGURE 1.9 Probability Space for the Default Probability of a Single Loan to W

CASE B The commercial bank C has lent $5,000,000 to company X and $5,000,000 to company Y. Let’s assume both X and Y have a 10% default probability. So C’s concentration ratio is reduced to ½. If the default correlation between X and Y is bigger than 0 and smaller than 1, we derive that the worst-case scenario [i.e., the default of X and Y, P(X ∩ Y), with a loss of $1,000,000] is reduced, as seen in Figure 1.10. The exact joint default probability P(X ∩ Y) depends on the correlation model and correlation parameter values, which will be discussed in Chapters 3 to 8. For any model, though, if default correlation between X and Y is 1, then there is no benefit from the lower concentration ratio. The probability space would have the form as in Figure 1.9.

P(X

PX = 10%

Y)

PY = 10%

FIGURE 1.10 Probability Space for Loans to Companies X and Y (continued)

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CORRELATION RISK MODELING AND MANAGEMENT

(continued) P(X

Y

Z)

PY = 10%

PX = 10% PZ = 10%

FIGURE 1.11 Probability Space for Loans to Companies X, Y, and Z CASE C If we further decrease the concentration ratio, the worst-case scenario (i.e., the expected loss of 10%) decreases further. Let’s assume the lender C gives loans to three companies, X, Y, and Z, of $3.33 million each. Let’s assume that the default probabilities of X, Y, and Z are 10% each. Therefore the concentration ratio decreases to 1/3. The probabilities are displayed in Figure 1.11. Hence from Figures 1.9 to 1.11 we observe the benefits of a lower concentration ratio. The worst-case scenario, an expected loss of $1,000,000, reduces with a decreasing concentration ratio. A decreasing concentration ratio is closely related to a decreasing correlation coefficient. Let’s show this. The defaults of companies X and Y are expressed as two binomial variables, which take the value 1 if in default, and 0 otherwise. Equation (1.11) gives the joint probability of default for the two binomial events: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (1.11) P(X ∩ Y) = rXY PX (1 − PX )PY (1 − PY ) + PX PY where rXY is the correlation coefficient and pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PX (1 − PX )

(1.12)

is the standard deviation of the binomially distributed variable X. Let’s assume again that the lender C has given loans to X and Y of $5,000,000 each. Both X and Y have a default probability of 10%. Followingpequation (1.12), this means that the standard deviation for X ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi and Y is 0:1 ´ (1 − 0:1) = 0:3.

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Let’s first look at the case where the default correlation is rXY = 1. This means that X and Y cannot default individually. They can only default together or survive together. The probability that they default together is 10%. Hence the expected loss is the same as in case A: EL = ($5,000,000 + $5,000,000) ´ 0.1 = $1,000,000. We can verify this with equation (1.11) for the joint probability of two binomial events, pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P(X ∩ Y) = 1 ´ 0:1 (1 − 0:1) ´ 0:1 (1 − 0:1) + 0:1 ´ 0:1 = 10%. The probability space is graphically the same as Figure 1.9 with PX = PY = 10% as the probability event. If we now decrease the correlation coefficient, we can see from equation (1.11) that the worst-case scenario, the joint default probability of X and Y, P(X ∩ Y), will decrease. For example, rXY = 0.5 results in P(X ∩ Y) = 5:5%, and rXY = 0 results in P(X ∩ Y) = 1%. Interestingly, even a slightly negative correlation coefficient can result in a positive joint default probability if the standard deviation of the binomial events is fairly low and the default probabilities are high. In our example, the standard deviation of both entities is 30% and a default probability of both entities is 10%. Together with a negative correlation coefficient of −0.1, following equation (1.11), this leads to a joint default probability of 0.1%. We will discuss the binomial correlation model in more detail in Chapter 4, section 4.2. In conclusion, we have shown the beneficial aspect of a lower concentration ratio, which is closely related to a lower correlation coefficient. In particular, both a lower concentration ratio and a lower correlation coefficient reduce the worst-case scenario for a creditor, the joint probability of default of his debtors. In Chapter 12, section 12.2, we will verify this result and find that a higher (copula) correlation between assets results in a higher credit value at risk (CVaR). CVaR measures the maximum loss of a portfolio of correlated debt with a certain probability for a certain time frame. Hence CVaR measures correlated default risk and is analogous to the VaR concept for correlated market risk, which we discussed in section 1.3.

1.5 A WORD ON TERMINOLOGY As mentioned in section 1.3.2, “Trading and Correlation,” we find the terms correlation desks or correlation trading in trading practice. Correlation trading means that traders trade assets or execute trading strategies whose value is at least in part determined by the comovement of two or more assets

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CORRELATION RISK MODELING AND MANAGEMENT

in time. We already mentioned pairs trading, the exchange option, and the quanto option as examples of correlation trading. In trading practice, the term correlation is typically applied quite broadly, referring to any comovement of asset prices in time. However, in financial theory, especially in recent publications, the term correlation is often defined more narrowly, referring only to the linear Pearson correlation model, as in Cherubini, Luciano, and Vecchiato (2004), Nelsen (2006), or Gregory (2010). These authors refer to other than Pearson correlation coefficients as dependence measures or measures of association. However, in financial theory the term correlation is also often applied generally to describe dependencies, as in the terms credit correlation, default correlation, or volatility-asset return correlation, which are quantified by non-Pearson models as in Heston (1993), Lucas (1995), or Li (2000). In this book, we will refer to the Pearson coefficient, discussed in Chapter 3, section 3.1, as correlation coefficient and the coefficients derived by nonPearson models as dependency coefficients. In accordance with most literature, we will refer to all methodologies that measure some form of dependency as correlation models or dependency models. Ordinal dependence measures, discussed in sections 3.2 and 3.3, which are related to the Pearson correlation approach, will be termed rank correlation measures.

1.6 SUMMARY There are two types of financial correlations: (1) Static correlations measure how two or more financial assets are associated within a certain time period, for example a year. (2) Dynamic financial correlations measure how two or more financial assets move together in time. Correlation risk can be defined as the risk of financial loss due to adverse movements in correlation between two or more variables. These variables can be financial variables such as correlated defaults of two debtors or nonfinancial such as the correlation between political tensions and an exchange rate. Correlation risk can be nonmonotonous, meaning that the dependent variable, for example the CDS spread, can sometimes increase and sometimes decrease when the correlation parameter value increases. Correlations and correlation risk are critical in many areas in finance, such as investments, trading, and especially in risk management, where different correlations result in very different degrees of risk. Correlations also play a key role in a systemic crisis, where correlations typically increase and can lead to high unexpected losses. As a result, the Basel III accord has introduced several correlation concepts and measures to reduce correlation risk (see Chapter 12 for details).

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Some Correlation Basics: Properties, Motivation, Terminology

Correlation risk can be categorized as its own type of risk. However, correlation parameters and correlation matrices are critical inputs and hence a part of market risk and credit risk. Market risk and credit risk are highly sensitive to changing correlations. Correlation risk is also closely related to concentration risk, as well as systemic risk, since correlations typically increase in a systemic crisis. The term correlation is not uniquely defined. In trading practice, correlation is applied quite broadly and refers to the comovements of assets in time, which may be measured by different correlation concepts. In financial theory, the term correlation is often defined more narrowly, referring only to the linear Pearson correlation coefficient. Non-Pearson correlation measures are termed dependence measures or measures of association.

APPENDIX 1A: DEPENDENCE AND CORRELATION Dependence In statistics, two events are considered dependent if the occurrence of one affects the probability of another. Conversely, two events are considered independent if the occurrence of one does not affect the probability of another. Formally, two events, A and B, are independent if and only if the joint probability equals the product of the individual probabilities: P(A ∩ B) = P(A)P(B)

(1A.1)

Solving equation (1A.1) for P(A), we get P(A) =

P(A ∩ B) P(B)

Following the Kolmogorov definition P(A) =

P(A ∩ B) P(B)

º P(AjB); we derive

P(A ∩ B) = P(AjB) P(B)

(1A.2)

where P(A|B) is the conditional probability of A with respect to B. P(A|B) reads “probability of A given B.” In equation (1A.2), the probability of A, P(A), is not affected by B, since P(A) = P(A|B); hence the event A is independent from B.

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From equation (1A.2) we also derive P(B) =

P(A ∩ B) = P(BjA) P(A)

(1A.3)

Hence from equation (1A.1) it follows that A is independent from B and B is independent from A. Example of Statistical Independence The historical default probability of company A is P(A) = 3%, the historical default probability of company B is P(B) = 4%, and the historical joint probability of default is 3% ´ 4% = 0.12%. In this case P(A) and P(B) are independent. This is because from equation (1A.2), we have P(A) =

P(A ∩ B) 3% ´ 4% = P(AjB) = 3% = = 3% P(B) 4%

Since P(A) = P(A|B), the default probability of company A is independent from the default probability of company B. Using equation (1A.3), we can do the same exercise for the default probability of company B, which is independent from the default probability of company A.

Correlation As mentioned in section 1.5, the term correlation is not uniquely defined. In trading practice, the term correlation is used quite broadly, referring to any comovement of asset prices in time. In statistics, correlation is typically defined more narrowly and typically refers to the linear dependency derived in the Pearson correlation model. Let’s look at the Pearson covariance and relate it to the dependence discussed earlier. A covariance measures how strong the linear relationship between two variables is. These variables can be deterministic (meaning their outcome is known), as the historical default probabilities in equation 1A.1. For random variables (variables with an unknown outcome such as flipping a coin), the Pearson covariance is derived with expectation values: Cov(X; Y) = E‰(X − E(X))(Y − E(Y))Š = E(XY) − E(X)E(Y)

(1A.4)

where E(X) and E(Y) are the expected values of (X) and (Y) respectively, also known as the mean, and E(XY) is the expected value of the product of the random variables X and Y.

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The covariance in equation (1A.4) is not easy to interpret. Therefore, often a normalized covariance, the correlation coefficient, is used. The Pearson correlation coefficient r(XY) is defined as r(X; Y) =

Cov(X; Y) s(X)s(Y)

(1A.5)

where s(X) and s(Y) are the standard deviations of X and Y, respectively. While the covariance takes values between −∞ and +∞, the correlation coefficient conveniently takes values between −1 and +1.

Independence and Uncorrelatedness From equation (1A.1) above we find that the condition for independence of two random variables is E(XY) = E(X) E(Y). From equation (1A.4) we see that E(XY) = E(X) E(Y) is equal to a covariance of zero. Therefore, if two variables are independent, their covariance is zero. Is the reverse also true? Does a zero covariance mean independence? The answer is no. Two variables can have a zero covariance even when they are dependent! Let’s show this with an example. For the parabola Y = X2, Y is clearly dependent on X, since Y changes when X changes. However, the correlation of the function Y = X2 derived by equations (1A.4) or (1A.5) is zero! This can be shown numerically and algebraically. For a numerical derivation, see the simple spreadsheet “Dependence and Correlation.xlsm,” sheet 1, at www.wiley.com/go/correlationriskmodeling, under “Chapter 1.” Algebraically, we have from equation (1A.4): Cov(X; Y) = E(XY) − E(X)E(Y) Inputting Y = X2, we derive Cov(X; Y) = E(X X2 ) − E(X)E(X2 ) = E(X3 ) − E(X)E(X2 ) Let X be a uniform variable bounded in [−1, +1]. Then the mean E(X) is zero and we have Cov(X; Y) = 0 − 0 E(X2 ) = 0 For a numerical example, see the spreadsheet “Dependence and Correlation.xlsm,” sheet 2, at www.wiley.com/go/correlationriskmodeling under “Chapter 1.”

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CORRELATION RISK MODELING AND MANAGEMENT

In conclusion, the Pearson covariance or correlation coefficient can give values of zero; that is, it tells us that the variables are uncorrelated, even if the variables are dependent! This is because the Pearson correlation concept measures only linear dependence. It fails to capture nonlinear relationships. This shows the limitation of the Pearson correlation concept for finance, since most financial relationships are nonlinear. See Chapter 3 for a more detailed discussion on the Pearson correlation model.

APPENDIX 1B: ON PERCENTAGE AND LOGARITHMIC CHANGES In finance, growth rates are expressed as relative changes, (St − St−1)/St−1, where St and St−1 are the prices of an asset at time t and t − 1, respectively. For example, if St = 110, and St−1 = 100, the relative change is (110 − 100)/ 100 = 0.1 = 10%. We often approximate relative changes with the help of the natural logarithm: (St − St−1 )=St−1 ≈ ln (St =St−1 )

(1B.1)

This is a good approximation for small differences between St and St−1. Ln(St/St−1) is called a log return. The advantage of using log returns is that they can be added over time. Relative changes are not additive over time. Let’s show this in two examples. Example 1: A stock price at t0 is $100. From t0 to t1, the stock increases by 10%. Hence the stock increases to $110. From t1 to t2 the stock increases again by 10%. So the stock price increases to $110 ´ 0.1 = $121. This increase of 21% is higher than adding the percentage increases of 10% + 10% = 20%. Hence percentage changes are not additive over time. Let’s look at the log returns. The log return from t0 to t1 is ln(110/100) = 9.531%. From t1 to t2 the log return is ln(121/110) = 9.531%. When adding these returns, we get 9.531% + 9.531% = 19.062%. This is the same as the log return from t0 to t2; that is, ln(121/100) = 19.062%. Hence log returns are additive in time.15 Let’s now look at another, more extreme example. Example 2: A stock price in t0 is $100. It moves to $200 in t1 and back to $100 in t2. The percentage change from t0 to t1 is ($200 − $100)/$100 = 100%. 15. We could also have solved for the absolute value 121, which matches a logarithmic growth rate of 9.531%: ln(x/110) = 9.531%, or ln(x) − ln(110) = 9.531%, or ln(x) = ln(110) + 9.531%. Taking the power of e, we get e(ln(x)) = x = e(ln(110)+0.09531) = 121.

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The percentage change from t1 to t2 is ($100 − $200)/(200) = −50%. Adding the percentage changes, we derive +100% − 50% = +50%, although the stock has not increased from t0 to t2! Naturally, this type of performance measure is incorrect and not allowed in accounting. Log returns give the correct answer: The log return from t0 to t1 is ln(200/ 100) = 69.31%. The log return from t1 to t2 is ln(100/200) = −69.31%. Adding these log returns in time, we get the correct return of the stock price from t0 to t2 of 69.31% − 69.31% = 0%. These examples are displayed in the simple spreadsheet “Log returns.xlsx” at www.wiley.com/go/correlationriskmodeling, under “Chapter 1.”

PRACTICE QUESTIONS AND PROBLEMS 1. What two types of financial correlations exist? 2. What is wrong-way correlation risk or short wrong-way risk? 3. Correlations can be nonmonotonous. What does this mean? 4. Correlations are critical in many areas in finance. Name five. 5. High diversification is related to low correlation. Why is this considered one of the few free lunches in finance? 6. Create a numerical example and show why a lower correlation results in a higher return/risk ratio. 7. What is correlation trading? 8. What is pairs trading? 9. Name three correlation options in which a lower correlation results in a higher option price. 10. Name one correlation option where a lower correlation results in a higher option price. 11. Create a numerical example of a two-asset portfolio and show that a lower correlation coefficient leads to a lower VaR number. 12. Why do correlations typically increase in a systemic market crash? 13. In 2005, a correlation crisis with respect to CDOs occurred that led to huge losses for several hedge funds. What happened? 14. In the global financial crisis of 2007 to 2009, many investors in the presumably safe super-senior tranches got hurt. What exactly happened? 15. What is the main objective of the Basel III accord? 16. The Basel accords have no legal authority. Why do most developed countries implement them? 17. How is correlation risk related to market risk and to credit risk? 18. How is correlation risk related to systemic risk and to concentration risk?

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19. How can we measure the joint probability of occurrence of a binomial event as default or no default? 20. Can it be that two binomial events are negatively correlated but they have a positive probability of joint default? 21. What is value at risk (VaR) and credit value at risk (CVaR)? How are they related? 22. Correlation risk is quite broadly defined in trading practice, referring to any comovement of assets in time. How is the term correlation defined in statistics? 23. What do the terms measure of association and measure of dependence refer to in statistics?

REFERENCES AND SUGGESTED READINGS Acerbi, D., and D. Tasche. 2001. “Expected Shortfall: A Natural Coherent Alternative to Value at Risk.” www.bis.org/bcbs/ca/acertasc.pdf. Artzner, P., F. Delbaen, J. M. Eber, and D. Heath. 1997. “Thinking Coherently.” Risk 10(11): 68–71. Brownless, C., and R. Engle. 2012. “Volatility, Correlation and Tails in Systemic Risk Measurement.” Working paper. Cherubini, U., E. Luciano, and W. Vecchiato. 2004. Copula Methods in Finance. Hoboken, NJ: John Wiley & Sons. Economist, August 2008. “A Personal View of the Crisis, Confessions of a Risk Manager.” Gregory, J. 2010. Counterparty Credit Risk. Hoboken, NJ: John Wiley & Sons. Heston, S. (1993), “A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options,” The Review of Financial Studies 6:327–343. Hull, J. 2012. Risk Management and Financial Institutions. 3rd ed. Hoboken, NJ: John Wiley & Sons. Jones, S. 2009. “The Formula That Felled Wall St.” Financial Times, April 24, 2009. Jorion, P. 2006. Value at Risk: The New Benchmark for Managing Financial Risk. 3rd ed. Hoboken, NJ: John Wiley & Sons. Kettunen, J., and G. Meissner. 2006. “Valuing Default Swaps on Correlated LMM Processes.” Journal of Alternative Investments, Summer. Lucas, D. 1995. “Default Correlation and Credit Analysis.” Journal of Fixed Income 4:76–87. Li, D. 2000. “On Default Correlation: A Copula Approach,” Journal of Fixed Income 9:119–149. Markovich, N. 2007. Nonparametric Analysis of Univariate Heavy-Tailed Data. Hoboken, NJ: John Wiley & Sons. Markowitz, H. M. 1952. “Portfolio Selection.” Journal of Finance 7:77–91.

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McNeil, A., R. Frey, and P. Embrechts. 2005. Quantitative Risk Management: Concepts, Techniques, and Tools. Princeton, NJ: Princeton University Press. Meissner, G. 2005. “Credit Derivatives—Application, Pricing, and Risk Management,” Wiley-Blackwell. Nelsen, R. 2006. An Introduction to Copulas. 2nd ed. Springer, New York. Sharpe, W. F. 1964. “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk.” Journal of Finance 19:425–442. Sklar, A. 1959. “Fonctions de Répartition à n Dimensions et Leurs Marges,” Publications de l’Institut de Statistique de L’Université de Paris 8:229–231. Yamai, Y., and T. Yoshiba. 2002. “On the Validity of Value-at-Risk: Comparative Analyses with Expected Shortfall.” Monetary and Economic Studies, January: 57–86.

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Contents

Preface to the New Edition

vii

CHAPTER 1 The Credit Decision

1

CHAPTER 2 The Credit Analyst

37

CHAPTER 3 The Business of Banking

87

CHAPTER 4 Deconstructing the Bank Income Statement

155

CHAPTER 5 Deconstructing a Bank’s Balance Sheet

215

CHAPTER 6 Earnings and Profitability

261

CHAPTER 7 Asset Quality

337

CHAPTER 8 Management and Corporate Governance

415

CHAPTER 9 Capital

449

CHAPTER 10 Liquidity

493

CHAPTER 11 Country and Sovereign Risk

551

CHAPTER 12 Risk Management, Basel Accords, and Ratings

641

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vi

CONTENTS

CHAPTER 13 The Banking Regulatory Regime

717

CHAPTER 14 Crises: Banking, Financial, Twin, Economic, Debt, Sovereign, and Policy Crises

781

CHAPTER 15 The Resolution of Banking Crises

847

About the Authors

907

Index

909

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Preface to the New Edition

I

n early 1997, Jonathan Golin applied for a position of bank credit analyst with Thomson BankWatch. He had limited experience in financial analysis, let alone bank financial analysis, but Philippe Delhaise, then president of BankWatch’s Asia division, had long held the view that outstanding brains, good analytical skills, a passion for details, and a degree of latent skepticism were the best assets of a brilliant bank financial analyst. He immediately hired Jonathan. Jonathan joined a team of very talented senior analysts, among them Andrew Seiz, Damien Wood, Tony Watson, Paul Grela, and Mark Jones. Philippe and the Thomson BankWatch Asia team produced, as early as 1994 and 1995, forewarning reports on the weaknesses of Asia’s banking systems that led to the Asian crisis of 1997. After the crisis erupted, Philippe made countless presentations on all continents, and he conducted, with some of his senior analysts, a number of seminars on the Asian crisis. This led to a contract with John Wiley & Sons for Philippe to produce a book on the 1997 crisis that was very well received, and which we hope the reader will forgive us for quoting occasionally. When in 1999 John Wiley & Sons started looking for a writer who could put together a comprehensive bank credit analysis handbook, Philippe had neither the time nor the courage to embark on such a voyage, but he encouraged Jonathan to take the plunge with the support of unlimited access to Philippe’s notes and experience, something Jonathan gave him credit for in the first edition of the Bank Credit Analysis Handbook, published in 2001. Meanwhile, Thomson BankWatch—at one point renamed Thomson Financial BankWatch—merged with Fitch in 2000, but Philippe and Jonathan quit prior to the merger. Philippe carried on teaching finance and conducting seminars on bank risk management in a number of countries. Recently, in Hong Kong, Philippe cofounded CTRisks Rating, a new rating agency using advanced techniques in the analysis of risk. Jonathan moved to London, where he founded two companies devoted to bank and company risk analysis. During the 2000s, the risk profile of most banks changed dramatically. Many changes took place in the manner banks had to manage and report their own risks, and in the way such risks shaped a bank’s own credit risk, as seen from the outside. Jonathan’s book needed an overhaul rather than a cosmetic update. This is how eventually Jonathan and Philippe joined forces to present this new, expanded edition to our readers. In the preface of the first edition, Jonathan thanked Darren Stubing for his substantial contribution to several chapters, and most likely some of Darren’s original input still pervades this new version of the book. The same applies to texts contributed by Andrew Seiz in the first edition, and there is no doubt that research done by the Thomson BankWatch Asia team, together with some of their New York–based

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viii

PREFACE TO THE NEW EDITION

colleagues, permeates the analytical line adopted both in Jonathan’s first edition and in the present new edition of The Bank Credit Analysis Handbook. The only direct outside contribution to this edition is coming from Richard Lumley in the chapter on risk management. We are thankful to all direct and indirect contributors.

DRAMATIC CHANGES The crisis that started in 2007 is still on at the time of writing. Banks and financial systems should share the blame with profligate politicians, outdated socioeconomic models, and a shift of the world’s center of gravity toward newcomers. However deep the resentment against banking and finance—often fanned by otherwise entertaining political slogans1—banks are here to stay. Banks remain a major conduit for the transformation of savings into productive investments. It is particularly so in emerging countries where capital markets are still not sufficiently developed and where savers have limited access to direct credit risk opportunities. Even in advanced economies, access to market risk often involves dealing with banks whose contribution as intermediaries is sometimes—and often justifiably—questionable. More than most other financial intermediaries, banks do carry substantial credit and market risks. They act as shock absorbers by removing from their depositor’s shoulders—and charging, alas, hefty fees for the service—some of that burden. As we shall point out in this book, weak banks actually rarely fail—they often merge or get nationalized—or at least their problems rarely translate into losses for depositors2 or creditors. Major disasters do occur, though, and we should not dismiss the view that the mere possibility of such an occurrence is enough for state ownership or state control of banks to gain respect in spite of the huge inefficiencies such models introduce. At the very least, banks should be submitted, within reason, to better regulatory control. Banks, however, cannot survive unless they take risks. The trick for them is to manage those risks without destroying shareholder value—the fatter the better, from a creditworthiness point of view—and without endangering depositors and creditors.

STRUCTURE OF THE BOOK This book explores the tools available to external analysts who wish to find out for themselves whether and to what extent a bank or a group of banks is creditworthy. It is a jungle out there. A wide range of theoretical research is available. Extreme opinions exist on most topics, making it difficult to reach a consensus on a middle ground where depositors, creditors, and regulators should confine the banking systems’ risk analysis. Our book is a modest attempt at balancing the wealth of research and opinions within a useful handbook for analysts, regulators, risk assessment offices, and finance students. Dividing bank credit analysis in separate chapters was a headache. Asset quality has an impact on earnings and on capital adequacy, liquidity on asset quality and earnings, management skills on asset quality, earnings on capital, accounting rules on earnings and capital—all on convoluted Möbius strips.

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Preface to the New Edition

ix

The first three chapters explore the notions associated with the credit decision, with the tools used in creditworthiness analysis and generally with the business of banking, more specifically with those activities that expose banks to risk. Chapters 4 and 5 explore the earnings—or income, or profit and loss—statement and the balance sheet of a bank, together with the increasingly important off-balance sheet. Those documents are the first documents an analyst will be confronted with. Except for the reader already familiar with bank financial statements, those chapters are essential to understand how the various activities of the bank find their way into the final published documents that disclose—and sometimes conceal or disguise—the facts, figures, and ratios that should shape the analyst’s opinion on the bank’s creditworthiness. The two accounting chapters pave the way for the introduction, in separate chapters, of the five basic elements of CAMEL, the mainstream model for assessing a bank’s performance and financial condition. Each of those five chapters relates back, in some way, to the two accounting chapters. Chapter 6 discusses earnings and profitability, with their many indicators. Chapter 7 is the most important as it attempts to describe how the analyst can assess the asset quality of a bank, and how the bank monitors its assets and deals with nonperforming loans and with its exposure to other impaired assets or transactions. Management and corporate governance are covered in Chapter 8, where the analyst will, among other things, learn how to appraise a bank’s overall management skills, which, in spite of tighter external regulations, remain a critical factor. Chapter 9 is about capital and its various definitions and indicators. This is where a first round of comments touches on the Basel Accords, because the earlier versions of those accords focused almost exclusively on capital adequacy. Liquidity, which is in Chapter 10, has become a major issue in the wake of the 2007–2012 crisis. It is also a very difficult parameter to analyze. No single indicator is able to describe a bank’s liquidity position, to the point where even the proposed liquidity requirements under Basel III do not bring much light to the debate. Chapter 11 is about country and sovereign risks, which used to be relevant only to emerging markets but came to the fore during the 2011–2012 debt crisis in Europe. Globalization and the free circulation of funds around most of the world have now pushed the analysis of country and sovereign risk way beyond the traditional ratios describing such basic factors as inflation or balance of payments. Bank creditworthiness is more than ever influenced by macroeconomic factors. Risk management is analyzed in Chapter 12, together with the second part of our exploration of the Basel Accords, to which we added a section on ratings. Risk management is no doubt the topic that saw the most changes over the past few years. The banking regulatory regime is explored in Chapter 13, with its structural and prudential regulations as well as its impact on systemic issues. The regional and worldwide crises of the past 20 years have generated considerable research on the causes of, and remedies to bank crises, financial crises, debt crises, sovereign crises, and their various combinations. Those crises are described and explained in Chapter 14, while Chapter 15—our last chapter—is devoted to the resolution of banking crises specifically. We decided against offering a glossary of financial terms, as the book is already heavy and, in this day and age, the reader will no doubt find excellent glossaries on the Internet.

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PREFACE TO THE NEW EDITION

In our attempt to render the reader’s task easier by dividing the book into 15 chapters, we created the need for many cross-references to other chapters. We believed that the reader would have neither the courage nor the need to swallow many chapters in one sitting, and we wanted, as much as possible, our chapter on, say, asset quality to cover most or all of what the reader would want to know when reading that chapter in isolation. Inevitably, as a result, there is a—small—degree of duplication here or there. We would like to beg our readers’ forgiveness for offering many examples from Asia. Both authors are thoroughly familiar with banking systems in that region— which admittedly is no justification in itself—while, more importantly, Asia is by far the largest financial market outside of the EU and the United States. In addition, whatever the definition of an emerging market, Asia without Japan arguably harbors the biggest emerging market banking system in the world, a fertile ground for dubious banking practices. Considerable research is available on banking systems, banking crises, and other topics relevant to the bank credit analyst. As a matter of fact, so much information and so many opinions are offered that the analyst would need to invest a year of her life just to get acquainted with the existing literature on bank creditworthiness. Our modest ambition was to distill academic research into something palatable, to pepper our findings with information gathered over our many years of experience in bank credit analysis, and to offer our reader a useful reference handbook. London and Port Arthur September 2012

NOTES 1. Malaysia’s Prime Minister Mahathir produced an interesting opinion in a speech on September 20, 1997 reported by the Manila Standard newspaper on September 22, 1997: “Currency trading is unnecessary, unproductive and immoral. . . . It should be illegal.” As reported in French by Le Parisien newspaper on January 12, 2012, socialist François Hollande said on that day in a meeting during his campaign for the French presidency “Dans cette bataille qui s’engage, mon véritable adversaire n’a pas de nom, pas de visage, pas de parti, mais il gouverne; cet adversaire c’est le monde de la finance,” which freely translates as: “In the battle that is starting, my true opponent has no name, no face, no party, but it reigns; this opponent is the world of finance.” 2. Especially so where deposit insurance schemes exist.

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CHAPTER

1

The Credit Decision CREDIT. Trust given or received; expectation of future payment for property transferred, or of fulfillment or promises given; mercantile reputation entitling one to be trusted;—applied to individuals, corporations, communities, or nations; as, to buy goods on credit. —Webster’s Unabridged Dictionary, 1913 Edition A bank lives on credit. Till it is trusted it is nothing; and when it ceases to be trusted, it returns to nothing. —Walter Bagehot1 People should be more concerned with the return of their principal than the return on their principal. —Jim Rogers2

T

he word credit derives from the ancient Latin credere, which means “to entrust” or “to believe.”3 Through the intervening centuries, the meaning of the term remains close to the original; lenders, or creditors, extend funds—or “credit”—based upon the belief that the borrower can be entrusted to repay the sum advanced, together with interest, according to the terms agreed. This conviction necessarily rests upon two fundamental principles; namely, the creditor’s confidence that: 1. The borrower is, and will be, willing to repay the funds advanced 2. The borrower has, and will have, the capacity to repay those funds The first premise generally relies upon the creditor’s knowledge of the borrower (or the borrower’s reputation), while the second is typically based upon the creditor’s understanding of the borrower’s financial condition, or a similar analysis performed by a trusted party.4

DEFINITION OF CREDIT Consequently, a broad, if not all-encompassing, definition of credit is the realistic belief or expectation, upon which a lender is willing to act, that funds advanced will

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THE BANK CREDIT ANALYSIS HANDBOOK

be repaid in full in accordance with the agreement made between the party lending the funds and the party borrowing the funds.5 Correspondingly, credit risk is the possibility that events, as they unfold, will contravene this belief.

SOME OTHER DEFINITIONS OF CREDIT Credit [is] nothing but the expectation of money, within some limited time. —John Locke Credit is at the heart of not just banking but business itself. Every kind of transaction except, maybe, cash on delivery—from billion-dollar issues of securities to getting paid next week for work done today— involves a credit judgment. . . . Credit . . . is like love or power; it cannot ultimately be measured because it is a matter of risk, trust, and an assessment of how flawed human beings and their institutions will perform. —R. Taggart Murphy6

Creditworthy or Not Put another way, a sensible individual with money to spare (i.e., savings or capital) will not provide credit on a commercial basis7—that is, will not make a loan—unless she believes that the borrower has both the requisite willingness and capacity to repay the funds advanced. As suggested, for a creditor to form such a belief rationally, she must be satisfied that the following two questions can be answered in the affirmative: 1. Will the prospective borrower be willing, so long as the obligation exists, to repay it? 2. Will the prospective borrower be able to repay the obligation when required under its terms? Traditional credit analysis recognizes that these questions will rarely be amenable to definitive yes/no answers. Instead, they call for a judgment of probability. Therefore, in practice, the credit analyst has traditionally sought to answer the question: What is the likelihood that a borrower will perform its financial obligations in accordance with their terms? All other things being equal, the closer the probability is to 100 percent, the less likely it is that the creditor will sustain a loss and, accordingly, the lower the credit risk. In the same manner, to the extent that the probability is below 100 percent, the greater the risk of loss, and the higher the credit risk.

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The Credit Decision

CASE STUDY: PREMODERN CREDIT ANALYSIS The date: The last years of the nineteenth century The place: A small provincial bank in rural England—let us call the institution Wessex Bank—located in the market town of Westport Simon Brown, a manager of Wessex Bank, is contemplating a loan to John Smith, a newly arrived merchant who has recently established a bicycle shop in the town’s main square. Smith’s business has only been established a year or so, but trade has been brisk, judging by the increasing number of two-wheelers that can be seen on Westport’s streets and in the surrounding countryside. Yesterday, Smith called on Brown at his office, and made an application for a loan. The merchant’s accounts, Brown noted, showed a burgeoning business, but one in need of capital to fund inventory expansion, especially in preparation for spring and summer, when prospective customers flock to the shop. While some of Smith’s suppliers provide trade credit, sharply increasing demand for cycles and limited supply have caused them to tighten their own credit terms. Smith projected, not entirely unreasonably, thought Brown, that he could increase his turnover by 30 percent if he could acquire more stock and promise customers quick delivery. When asked by Brown, Smith said he would be willing to pledge his assets, including the shop’s inventory, as collateral to secure the loan. But Brown, as befits his reputation as a prudent banker, remained skeptical. Those newfangled machines were, in his view, dangerous vehicles and very likely a passing fad. During the interview, Smith mentioned in passing that he was related on his father’s side to Squire Roberts, a prosperous local landowner well known to Brown and a longstanding customer of Wessex Bank. Just that morning, Brown had seen the old gentleman at the post office, and, to his surprise, Roberts struck up a conversation about the weather and the state of the timber trade, and mentioned that he had heard his nephew had called on Brown recently. Before Brown had time to register the news that Roberts was Smith’s uncle, Roberts volunteered that he was willing to vouch for Smith’s character— “a fine lad”—and, moreover, added that he was willing to guarantee the loan. Brown decided to have another look at Smith’s loan application. Rubbing his chin, he reasoned to himself that the morning’s news presented another situation entirely. Not only was Smith not the stranger he was before, but he was also a potentially good customer. With confirmation of his character from Roberts, Brown was on his way to persuading himself that the bank was probably adequately protected. Roberts’s indication that he would guarantee the loan removed any remaining doubts. Should Smith default, the bank could hold the well-off Roberts liable for the obligation. Through the prospective substitution of Robert’s creditworthiness for that of Smiths’s the bank’s credit risk was considerably reduced. The last twinge of anxiety having been removed, Brown decided to approve the loan to Smith.

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THE BANK CREDIT ANALYSIS HANDBOOK

Credit Risk Credit risk and the concomitant need for the estimation of that risk surface in many business contexts. It emerges, for example, when one party performs services for another and then sends a bill for the services rendered for payment. It also arises in connection with the settlement of transactionsâ&#x20AC;&#x201D;where one party has advanced payment to the other and awaits receipt of the items purchased or where one party has advanced the items purchased and awaits payment. Indeed, most enterprises that buy and sell products or services, that is practically all businesses, incur varying degrees of credit risk. Only in respect to the simultaneous exchange of goods for cash can it be said that credit risk is essentially absent. While nonfinancial enterprises, particularly small merchants, can eliminate credit risk by engaging only in cash and carry transactions, it is common for vendors to offer credit to buyers to facilitate a particular sale, or merely because the same terms are offered by their competitors. Suppliers, for example, may offer trade credit to purchasers, allowing some reasonable period of time, say 30 days, to settle an invoice. Risks arising from trade credit form a transition zone between settlement risk and the creation of a more fundamental financial obligation. It is evident that as opposed to trade credit, as well as settlement risk that emerges during the consummation of a sale or transfer, fundamental financial obligation arises where sellers offer explicit financing terms to prospective buyers. This type of credit extension is particularly common in connection with purchases of big ticket items by consumers or businesses. As an illustration, automobile manufacturers frequently offer customers attractive finance terms as an incentive. Similarly, a manufacturer of electrical generating equipment may offer financing terms to facilitate the sale of the machinery to a power utility company. Such credit risk is essentially indistinguishable from that created by a bank loan. In contrast to nonfinancial firms, which can choose to operate on a cash-only basis, banks by definition cannot avoid credit risk. The acceptance of credit risk is inherent to their operation since the very raison dâ&#x20AC;&#x2122;ĂŞtre of banks is the supply of credit through the advance of cash and the corresponding creation of financial obligations. Success in banking is attained not by avoiding risk but by effectively selecting and managing risk. In order to better manage risk, it follows that banks must be able to estimate the credit risk to which they are exposed as accurately as possible. This explains why banks almost invariably have a much greater need for credit analysis than do nonfinancial enterprises, for which, again by definition, the shouldering of credit risk exposure is peripheral to their main business activity.

Credit Analysis For purposes of practical analysis, credit risk may be defined as the risk of monetary loss arising from any of the following four circumstances: 1. The default of a counterparty on a fundamental financial obligation 2. An increased probability of default on a fundamental financial obligation 3. A higher than expected loss severity arising from either a lower than expected recovery or a higher than expected exposure at the time of default 4. The default of a counterparty with respect to the payment of funds for goods or services that have already been advanced (settlement risk)

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The Credit Decision

5

The variables most directly affecting relative credit risk include the following four: 1. The capacity and willingness of the obligor (borrower, counterparty, issuer, etc.) to meet its obligations 2. The external environment (operating conditions, country risk, business climate, etc.) insofar as it affects the probability of default, loss severity, or exposure at default 3. The characteristics of the relevant credit instrument (product, facility, issue, debt security, loan, etc.) 4. The quality and sufficiency of any credit risk mitigants (collateral, guarantees, credit enhancements, etc.) utilized Credit risk is also influenced by the length of time over which exposure exists. At the portfolio level, correlations among particular assets together with the level of concentration of particular assets are the key concerns.

Components of Credit Risk At the level of practical analysis, the process of credit risk evaluation can be viewed as formulating answers to a series of questions with respect to each of these four variables. The following questions are intended to be suggestive of the line of inquiry that might be pursued. The Obligorâ&#x20AC;&#x2122;s Capacity and Willingness to Repay n What is the capacity of the obligor to service its financial obligations? n How likely will it be to fulfill that obligation through maturity? n What is the type of obligor and usual credit risk characteristics associated with its business niche? n What is the impact of the obligorâ&#x20AC;&#x2122;s corporate structure, critical ownership, or other relationships and policy obligations upon its credit profile? The External Conditions n

n

How do country risk (sovereign risk) and operation conditions, including systemic risk, impinge upon the credit risk to which the obligee is exposed? What cyclical or secular changes are likely to affect the level of that risk? The obligation (product): What are its credit characteristics?

The Attributes of Obligation from Which Credit Risk Arises n What are the inherent risk characteristics of that obligation? Aside from general legal risk in the relevant jurisdiction, is the obligation subject to any legal risk specific to the product? n What is the tenor (maturity) of the product? n Is the obligation secured; that is, are credit risk mitigants embedded in the product? n What priority (e.g., senior, subordinated, unsecured) is assigned to the creditor (obligee)? n How do specific covenants and terms benefit each party thereby increasing or decreasing the credit risk to which the obligee is exposed? For example, are there

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THE BANK CREDIT ANALYSIS HANDBOOK

n n

any call provisions allowing the obligor to repay the obligation early; does the obligee have any right to convert the obligation to another form of security? What is the currency in which the obligation is denominated? Is there any associated contingent/derivative risk to which either party is subject?

The Credit Risk Mitigants n Are any credit risk mitigantsâ&#x20AC;&#x201D;such as collateralâ&#x20AC;&#x201D;utilized in the existing obligation or contemplated transaction? If so, how do they impact credit risk? n If there is a secondary obligor, what is its credit risk? n Has an evaluation of the strength of the credit risk mitigation been undertaken? In this book, our primary focus will be on the obligor bank and the environment in which it operates, with consideration of the credit characteristics of specific financial products and accompanying credit risk mitigants relegated to a secondary position. The reasons are twofold. One, evaluation of the first two elements form the core of bank credit analysis. This is invariably undertaken before adjustments are made to take account of the impact of the credit characteristics of particular financial products or methods used to modify those characteristics. Two, to do justice to the myriad of different types of financial products, not to speak of credit risk mitigation techniques, requires a book in itself and the volume of material to be covered with regard to the obligor and the operating environment is greater than a single volume.

Credit Risk Mitigation While the foregoing query concerning the likelihood that a borrower will perform its financial obligations is simple, its simplicity belies the intrinsic difficulties in arriving at a satisfactory, accurate, and reliable answer. The issue is not just the underlying probability of default, but the degree of uncertainty associated with forecasting this probability. Such uncertainty has long led lenders to seek security in the form of collateral or guarantees, both to mitigate credit risk and, in practice, to circumvent the need to analyze it altogether. Collateralâ&#x20AC;&#x201D;Assets That Function to Secure a Loan Collateral refers to assets that are either deposited with a lender, conditionally assigned to the lender pending full repayment of the funds borrowed, or more generally to assets with respect to which the lender has the right to obtain title and possession in full or partial satisfaction of the corresponding financial obligation. Thus, the lender who receives collateral and complies with the applicable legal requirements becomes a secured creditor, possessing specified legal rights to designated assets in case the borrower is unable to repay its obligation with cash or with other current assets.8 If the borrower defaults, the lender may be able to seize the collateral through foreclosure9 and sell it to satisfy outstanding obligations. Both secured and unsecured creditors may force the delinquent borrower into bankruptcy. The secured creditor, however, benefits from the right to sell the collateral without necessarily initiating bankruptcy proceedings, and stands in a better position than unsecured creditors once such proceedings have commenced.10

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The Credit Decision

It is evident that, since collateral may generally be sold on the default of the borrower (the obligor), it provides security to the lender (the obligee). The prospective loss of collateral also gives the obligor an incentive to repay its obligation. In this way, the use of collateral tends to lower the probability of default, and, more significantly, reduce the severity of the creditor’s loss in the event of default, by providing the creditor with full or partial recompense for the loss that would otherwise be incurred. Overall, collateral tends to reduce, or mitigate, the credit risk to which the lender is exposed, and it is therefore classified as a credit risk mitigant.

COLLATERAL AND OTHER CREDIT RISK MITIGANTS Credit risk mitigants are devices such as collateral, pledges, insurance, or guarantees that may be used to reduce the credit risk exposure to which a lender or creditor would otherwise be subject. The purpose of credit risk mitigants is partially or totally to ameliorate a borrower’s lack of intrinsic creditworthiness and thereby reduce the credit risk to the lender, or to justify advancing a larger sum than otherwise would be contemplated. For instance, a lender may require a guarantee where the borrower is comparatively new or lacks detailed financial statements but the guarantor is a well-established enterprise rated by the major external agencies. In the past, these mechanisms were frequently used to reduce or eliminate the need for the credit analysis of a prospective borrower by substituting conservatively valued collateral or the creditworthiness of an acceptable guarantor for the primary borrower. In modern financial markets, collateral and guarantees, rather than being substitutes for inadequate stand-alone creditworthiness, may actually be a requisite and integral element of the contemplated transaction. Their essential function is unchanged, but instead of remedying a deficiency, they are used to increase creditworthiness to give the transaction certain predetermined credit characteristics. In these circumstances, rather than eliminating the need for credit analysis, consideration of credit risk mitigants supplements, and sometimes complicates it. Real-life credit analysis consequently requires an integrated approach to the credit decision, and typically requires some degree of analysis of both the primary borrower and of the impact of any applicable credit risk mitigants.

Since the amount advanced is known, and because collateral can normally be appraised with some degree of accuracy—often through reference to the market value of comparable goods or assets—the credit decision is considerably simplified. By obviating the need to consider the issues of the borrower’s willingness and capacity, the question—What is the likelihood that a borrower will perform its financial obligations in accordance with their terms?—can be replaced with one more easily answered, namely: “Will the collateral provided by the prospective borrower be sufficient to secure repayment?”11

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THE BANK CREDIT ANALYSIS HANDBOOK

As Roger Hale, the author of an excellent introduction to credit analysis, succinctly puts it: “If a pawnbroker lends money against a gold watch, he does not need credit analysis. He needs instead to know the value of the watch.”12 Guarantees A guarantee is the promise by a third party to accept liability for the debts of another in the event that the primary obligor defaults, and is another kind of credit risk mitigant. Unlike collateral, the use of a guarantee does not eliminate the need for credit analysis, but simplifies it by making the guarantor instead of the borrower the object of scrutiny. Typically, the guarantor will be an entity that either possesses greater creditworthiness than the primary obligor, or has a comparable level of creditworthiness but is easier to analyze. Often, there will be some relationship between the guarantor and the party on whose behalf the guarantee is provided. For example, a father may guarantee a finance company’s loan to his son13 for the purchase of a car. Likewise, a parent company may guarantee a subsidiary’s loan from a bank to fund the purchase of new premises. Where a guarantee is provided, the questions posed with reference to the prospective borrower must be asked again in respect of the prospective guarantor: “Will the prospective guarantor be both willing to repay the obligation and have the capacity to repay it?” These questions are summarized in Exhibit 1.1. EXHIBIT 1.1 Key Credit Questions

Willingness to pay

Primary Subject of Analysis (e.g., borrower)

Binary (Yes/No)

Probability

Will the prospective borrower be willing to repay the funds?

What is the likelihood that a borrower will perform its financial obligations in accordance with their terms?

Capacity to pay

Will the prospective borrower be able to repay the funds?

Collateral

Will the collateral provided by the prospective borrower or the guarantees given by a third party be sufficient to secure repayment?

What is the likelihood that the collateral provided by the prospective borrower or the guarantees given by a third party will be sufficient to secure repayment?

Guarantees

Will the prospective guarantor be willing to repay the obligation as well as have the capacity to repay it?

What is the likelihood that the prospective guarantor will be willing to repay the obligation as well as have the capacity to repay it?

Secondary Subject of Analysis (Credit risk mitigants)

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The Credit Decision

SigniďŹ cance of Credit Risk Mitigants In view of the benefits of using collateral and guarantees to avoid the sometimes thorny task of performing an effective financial analysis,14 banks and other institutional lenders traditionally have placed primary emphasis on these credit risk mitigants, and other comparable mechanisms such as joint and several liability15 when allocating credit.16 For this reason, secured lending, which refers to the use of credit risk mitigants to secure a financial obligation as discussed, remains a favored method of providing financing. In countries where financial disclosure is poor or the requisite analytical skills are lacking, credit risk mitigants circumvent some of the difficulties involved in performing an effective credit evaluation. In developed markets, more sophisticated approaches to secured lending such as repo finance and securities lending17 have also grown increasingly popular. In these markets, however, the use of credit risk mitigants is often driven by the desire to facilitate investment transactions or to structure credit risks to meet the needs of the parties to the transaction rather than to avoid the process of credit analysis. With the evolution of financial systems, credit analysis has become increasingly important and more refined. For the moment, though, our focus is upon credit evaluation in its more basic and customary form.

WILLINGNESS TO PAY Willingness to pay is, of course, a subjective attribute that can be ascertained to a degree from the borrowerâ&#x20AC;&#x2122;s reputation and apparent character. Assuming free will,18 it is also essentially unknowable in advance, even perhaps to the borrower. From the perspective of the lender or credit analyst, the evaluation is therefore necessarily a qualitative one that takes into account information gleaned from a variety of sources, including, where possible, face-to-face meetings that are a customary part of the process of due diligence.19 The old-fashioned provincial banker who was familiar with local business conditions and prospective borrowers, like the fictional character described earlier, had less need for formal credit analysis. Instead, the intuitive judgment that came from an in-depth knowledge of a community and its members was an invaluable attribute in the banking industry. The traditional banker knew with whom he was dealing (or thought he did), either locally with his customers or at a distance with correspondent banks20 that he trusted. Walter Bagehot, the nineteenth-century British economic commentator put it well: A banker who lives in the district, who has always lived there, whose whole mind is a history of the district and its changes, is easily able to lend money there. But a manager deputed by a central establishment does so with difficulty. The worst people will come to him and ask for loans. His ignorance is a mark for all the shrewd and crafty people thereabouts.21 In general, modern credit analysis still takes account of willingness to pay, and in doing so maintains an unbroken link with its past. It is still up to one or more

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THE BANK CREDIT ANALYSIS HANDBOOK

individuals to decide whether to extend or to repay a debt, and manuals on banking and credit analysis as a rule make some mention of the importance of taking account of a prospective borrower’s character.22

Indicators of Willingness Willingness to pay, though real, is difficult to assess. Ultimately, judgments about this attribute, and the criteria on which they are based, are highly subjective in nature. Character and Reputation First-hand awareness of a prospective borrower’s character affords at least a steppingstone on which to base a credit decision. Where direct familiarity is lacking, a sense of the borrower’s reputation provides an alternative footing upon which to ascertain the obligor’s disposition to make good on a promise. Reliance on reputation can be perilous, however, since a dependence upon second-hand information can easily descend into so-called name lending.23 Name lending can be defined as the practice of lending to customers based on their perceived status within the business community instead of on the basis of facts and sound conclusions derived from a rigorous analysis of the prospective borrowers’ actual capacity to service additional debt. Credit Record Although far more data is available today than a century ago, assessing a borrower’s integrity and commitment to perform an obligation still requires making unverifiable, even intuitive, judgments. Rather than put a foot wrong into a miasma of imponderables, creditors have long taken a degree of comfort not only in collateral and guarantees, but also in a borrower’s verifiable history of meeting its obligations. As compared with the prospective borrower who remains an unknown quantity, a track record of borrowing funds and repaying them suggests that the same pattern of repayment will continue in the future.24 If available, a borrower’s payment record, provided for example through a credit bureau, can be an invaluable resource for a creditor. Of course, while the past provides some reassurance of future willingness to pay, here as elsewhere, it cannot be extrapolated into the future with certainty in any individual case.25

Creditors’ Rights and the Legal System While the ability to make the requisite intuitive judgments concerning willingness to pay probably comes more easily to some than to others, and no doubt may be honed with experience, perhaps fortunately it has become less important in the credit decision-making process.26 The concept of a moral obligation27 to repay a debt— which perhaps in the past arguably bolstered the will of the faltering borrower to perform his obligation in full—has been to a large extent displaced in contemporary commerce by legal rather than ethical norms. It is logical to rank capacity to pay as more important than willingness, since willingness alone is of little value where capacity is absent. Capacity without willingness, however, can be overcome to a large degree through an effective legal system.28 The stronger and more effectual the legal infrastructure, the better able a creditor is to enforce a judgment against a borrower.29 Prompt court decisions backed by the threat of the seizure of possessions or other means through the arm of

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The Credit Decision

the state will tend to predispose the nonperforming debtor to fulfill its obligations. A borrower who can pay but will not, is only able to maintain such a position in a legal regime that is ineffective or corrupt, or very strongly favors debtors over creditors. So as legal systems have developed—along with the evolution of financial analytical techniques and data collection and distribution systems—the attribute of willingness to repay has been increasingly overshadowed in importance by the attribute of capacity to repay. It follows that the more a legal system exhibits creditor-friendly characteristics—combined with the other critical attributes of integrity, efficiency, and judges’ understanding of commercial requirements—the less the lender needs to rely upon the borrower’s willingness to pay, and the more important the capacity to repay becomes. The development of capable legal systems has therefore increased the importance of financial analysis and as a prerequisite to it, financial disclosure. Overall, the evolution of more robust and efficient legal systems has provided a net benefit to creditors.30 Willingness to pay, however, remains a more critical criterion in less-developed markets, where the quality of the legal framework may be lacking. In these instances, the efficacy of the legal system in protecting creditors’ rights also emerges as an important criterion in the analytical process.31

CREDIT ANALYSIS IN EMERGING MARKETS: THE IMPORTANCE OF THE LEGAL SYSTEM Weak legal and regulatory infrastructure and concomitant doubts concerning the fair and timely enforcement of creditors’ rights mean that credit analysis in so-called emerging markets32 is often more subjective than in developed markets. Due consideration must be given in these jurisdictions not only to a prospective borrower’s willingness to pay, but equally to the quality of the legal system. Since, as a practical matter, willingness to pay is inextricably linked to the variables that may affect the lender’s ability to coerce payment through legal redress, it is useful to consider, as part of the analytical process, the overall effectiveness and creditor-friendliness of a country’s legal infrastructure. Like the evaluation of an individual borrower’s willingness to pay, an evaluation of the quality of a legal system and the strength of a creditor’s rights is a highly qualitative endeavor. Despite the not inconspicuous inadequacies in the legal frameworks of the countries in which they extend credit, bankers during periods of economic expansion have time and again paid insufficient attention to prospective problems they might confront when a boom turns to bust. Banks have faced criticism for placing an undue reliance upon expectations of government support or, where the government itself is vulnerable to difficulties, upon the International Monetary Fund (IMF). Believing that the IMF would stand ready to provide aid to the governments concerned and thereby indirectly to the borrowers and to their creditors, it has been asserted that banks have engaged in imprudent lending. Insofar as such reliance has occurred, it has arguably been accompanied by a degree of obliviousness on the part of creditors to the difficulties involved in enforcing their rights through legal action.33

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THE BANK CREDIT ANALYSIS HANDBOOK

While the quality of a country’s legal system is a real and significant attribute, measuring it is no simple task. Traditionally, sovereign risk ratings functioned as a proxy for, among other things, the legal risk associated with particular geographic markets. Countries with low sovereign ratings were often implicitly assumed to be subject to a greater degree of legal risk, and vice versa. In the past 15 years, however, surveys have been conducted in an attempt systematically to grade, if not measure, comparative legal risk. Although by and large these studies have been initiated for purposes other than credit analysis—to assess a country’s investment climate, for instance—they would seem to have some application to the evaluation of credit risk. The table in Exhibit 1.2 shows the scores under such an index of rule of law. Some banks have used one or more similar surveys, sometimes together with other criteria, to generate internal creditors’ rights ratings for the jurisdictions in which they operate or in which they contemplate credit exposure.

EXHIBIT 1.2 Rule of Law Index: Selected Countries 2010 Country Finland Sweden Norway Denmark New Zealand Luxembourg Netherlands Austria Canada Switzerland Australia United Kingdom Ireland Greenland Singapore Iceland Germany Liechtenstein United States Hong Kong Sar, China France Malta Anguilla Aruba Belgium Japan Chile Andorra Spain Cyprus French Guiana American Samoa

Score 1.97 1.95 1.93 1.88 1.86 1.82 1.81 1.80 1.79 1.78 1.77 1.77 1.76 1.72 1.69 1.69 1.63 1.62 1.58 1.56 1.52 1.48 1.42 1.42 1.40 1.31 1.29 1.23 1.19 1.19 1.18 1.16

Country Bermuda Guam Estonia Portugal Barbados Slovenia Tuvalu Taiwan, China Korea, South Antigua and Barbuda Czech Republic Monaco San Marino Martinique Netherlands Antilles Reunion Virgin Islands (U.S.) Cayman Islands Israel Qatar St. Vincent and the Grenadines Mauritius St. Lucia Latvia Brunei Hungary Puerto Rico Lithuania Palau Uruguay St. Kitts And Nevis Macao Sar, China

Score 1.16 1.16 1.15 1.04 1.04 1.02 1.02 1.01 0.99 0.98 0.95 0.90 0.90 0.89 0.89 0.89 0.89 0.89 0.88 0.87 0.86 0.84 0.82 0.82 0.80 0.78 0.77 0.76 0.74 0.72 0.71 0.71

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The Credit Decision

Country

Score

Country

Score

Dominica Poland Bahamas Oman Botswana Samoa Greece Slovakia Kuwait Malaysia Costa Rica Bahrain Cape Verde Nauru United Arab Emirates Italy Vanuatu Namibia Jordan Croatia Saudi Arabia Grenada Tunisia Bhutan Turkey South Africa Tonga Kiribati Romania Seychelles Brazil Montenegro India Ghana Micronesia Bulgaria Sri Lanka Suriname Egypt Panama Malawi Morocco Thailand West Bank Gaza Georgia Burkina Faso Trinidad and Tobago Marshall Islands Macedonia Lesotho

0.69 0.69 0.68 0.67 0.66 0.65 0.62 0.58 0.54 0.51 0.50 0.45 0.42 0.41 0.39 0.38 0.25 0.23 0.22 0.19 0.16 0.11 0.11 0.11 0.10 0.10 0.09 0.07 0.05 0.02 0.00 0.02 0.06 0.07 0.08 0.08 0.09 0.09 0.11 0.13 0.14 0.19 0.20 0.20 0.21 0.21 0.22 0.27 0.29 0.30

Rwanda Maldives Colombia China Bosnia–Herzegovina Belize Serbia Moldova Uganda Senegal Mongolia Albania Mali Armenia Guyana Vietnam Zambia Swaziland Jamaica Mozambique Tanzania Gambia Gabon Syria Philippines Cuba Mexico Niger Argentina Peru Kazakhstan Indonesia Kosovo Lebanon São Tomé and Principe Solomon Islands Djibouti Niue Benin Ethiopia Algeria Bangladesh Russia Pakistan Ukraine Dominican Republic Nicaragua Madagascar Honduras El Salvador

0.31 0.33 0.33 0.35 0.36 0.36 0.39 0.40 0.40 0.41 0.43 0.44 0.46 0.47 0.48 0.48 0.49 0.50 0.50 0.50 0.51 0.51 0.51 0.54 0.54 0.55 0.56 0.57 0.58 0.61 0.62 0.63 0.64 0.66 0.69 0.70 0.71 0.72 0.73 0.76 0.76 0.77 0.78 0.79 0.80 0.81 0.83 0.84 0.87 0.87 (Continued)

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THE BANK CREDIT ANALYSIS HANDBOOK

EXHIBIT 1.2 (Continued) Country

Score

Country

Score

Mauritania Azerbaijan Laos Cook Islands Iran Fiji Paraguay Togo Papua New Guinea Sierra Leone Libya Liberia Kenya Nepal Guatemala Cameroon Belarus Yemen Comoros Bolivia Cambodia Congo Ecuador Tajikistan

0.88 0.88 0.90 0.90 0.90 0.90 0.92 0.92 0.93 0.94 0.98 1.01 1.01 1.02 1.04 1.04 1.05 1.05 1.06 1.06 1.09 1.13 1.17 1.20

Nigeria Timor–Leste Burundi Côte d’Ivoire Angola Equatorial Guinea Eritrea Kyrgyzstan Korea, North Central African Republic Sudan Guinea–Bissau Haiti Uzbekistan Turkmenistan Chad Myanmar Guinea Congo, Democratic Republic Iraq Venezuela Zimbabwe Afghanistan Somalia

1.21 1.21 1.21 1.22 1.24 1.26 1.29 1.29 1.30 1.30 1.32 1.35 1.35 1.37 1.46 1.50 1.50 1.51 1.61 1.62 1.64 1.80 1.90 2.43

Source: World Bank.

It is almost invariably the case that the costs of legal services are an important variable to be considered in any decision regarding the recovery of money owed. A robust legal system is not necessarily a cost-effective one, since the expenses required to enforce a creditor’s rights are rarely insignificant. While a modicum of efficiency may exist, the costs of legal actions, including the time spent pursuing them, may well exceed the benefits. It therefore may not pay to take legal action against a delinquent borrower. This is particularly the case for comparatively small advances. As a result, even where creditors’ rights are strictly enforced, willingness to pay ought never to be entirely ignored as an element of credit analysis.

EVALUATING THE CAPACITY TO REPAY: SCIENCE OR ART? Compared with willingness to pay, the evaluation of capacity to pay lends itself more readily to quantitative measurement. So the application of financial analysis will generally go far in revealing whether the borrower will have the ability to fulfill outstanding obligations as they come due. Evaluating the capability of an entity to perform its financial obligations through a close examination of numerical data derived from its most recent and past financial statements forms the core of credit analysis.

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The Credit Decision

The Limitations of Quantitative Methods While an essential element of credit evaluation, the use of financial analysis for this purpose is subject to serious limitations. These include: n n

n

The historical character of financial data. The difficulty of making reasonably accurate financial projections based upon such data. The inevitable gap between financial reporting and financial reality.

Historical Character of Financial Data The first and most obvious limitation is that financial statements are invariably historical in scope, covering as they do past fiscal reporting periods, and are therefore never entirely up to date. Because the past cannot be extrapolated into the future with any certainty, except perhaps in cases of clear insolvency and illiquidity, the estimation of capacity remains just that: an estimate. Even if financial reports are comparatively recent, or forward looking, the preceding difficulty is not surmounted. Accurate financial forecasting is notoriously problematic, and, no matter how sophisticated, financial projections are highly vulnerable to errors and distortion. Small differences at the outset can engender an enormous range of values over time. Financial Reporting Is Not Financial Reality Perhaps the most significant limitation arises from the fact that financial reporting is an inevitably imperfect attempt to map an underlying economic reality into a usable but highly abbreviated condensed report. As with attempts to map a large spherical surface onto a flat projection, some degree of deformation is unavoidable, while the very process of distilling raw data into a work product small enough to be usable requires that some data be selected and other data be omitted. In short, not only do financial statements intrinsically suffer from some degree of distortion and omission, these deficiencies are also apt to be aggravated in practice. First, the rules of financial accounting and reporting are shaped by people and institutions having differing perspectives and interests. Influences resulting from that divergence are apt to aggravate these innate deficiencies. The rules themselves are almost always the product of compromises by committee that are, at heart, political in nature. Second, the difficulty of making rules to cover every conceivable situation means that, in practice, companies are frequently afforded a great deal of discretion in determining how various accounting items are treated. At best, such leeway may only potentially result in inaccurate comparisons; at worst, this necessary flexibility in interpretation and classification may be used to further deception or fraud. Finally, even the most accurate financial statements must be interpreted. In this context too, differing vantage points, experience, and analytical skill levels may result in a range of conclusions from the same data. For all these reasons, it should be apparent that even the seemingly objective evaluation of financial capacity retains a significant qualitative, and therefore subjective, component. While acknowledging both its limitations and subjective element, financial analysis remains at the core of

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THE BANK CREDIT ANALYSIS HANDBOOK

effective credit analysis. The associated techniques serve as essential and invaluable tools for drawing conclusions about a company’s creditworthiness, and the credit risk associated with its obligations. It is, nevertheless, crucial not to place too much faith in the quantitative methods of financial analysis in credit risk assessment, nor to believe that quantitative data or conclusions drawn from such data necessarily represent an objective truth. No matter how sophisticated, when applied for the purpose of the evaluation of credit risk, these techniques must remain imperfect tools that seek to predict an unknowable future.

Quantitative and Qualitative Elements Given these shortcomings, the softer more qualitative aspects of the analytical process should not be given short shrift. Notably, an evaluation of management— including its competence, motivation, and incentives—as well as the plausibility and coherence of its strategy remains an important element of credit analysis of both nonfinancial and financial companies.34 Indeed, as suggested in the previous subsection, not only is credit analysis both qualitative and quantitative in nature, but nearly all of its nominally quantitative aspects also have a significant qualitative element. While evaluation of willingness to pay and assessment of management competence obviously involve subjective judgments, so too, to a larger degree than is often recognized, do the presentation and analysis of a firm’s financial results. Credit analysis is as much art as it is science, and its successful application relies as much on judgment as it does on mathematics. The best credit analysis is a synthesis of quantitative measures and qualitative judgments. For reasons that will soon become apparent, this is particularly so in regard to financial institution credit analysis.

QUANTITATIVE METHODS IN CREDIT ANALYSIS These remain imperfect tools that aim to predict an unknowable future. Nearly all of the nominally quantitative techniques also have a significant qualitative element. To reach optimal effectiveness, credit analysis must therefore combine the effective use of quantitative tools with sound qualitative judgments.

Credit Analysis versus Credit Risk Modeling At this stage, it should be noted that there is an important distinction to be drawn between credit risk analysis, on the one hand, and credit risk modeling and credit risk management, on the other. The process of performing a counterparty credit analysis is quite different from that involved in modeling bank credit risk or in managing credit risk at the enterprise level. Consider, for example, the concept of rating migration risk. Rating migration risk, while an important factor in modeling and evaluating portfolios of debt securities, is not, however, of concern to the credit analyst

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17

The Credit Decision

performing an evaluation of the kind upon which its rating has been based. It is important to recognize this distinction and to emphasize that the aim of the credit analyst is not to model credit risk, but instead to perform the evaluation that provides one of the requisite inputs to credit risk models. Needless to say, it is also one of the requisite inputs to the overall risk management of a banking organization.

RATING MIGRATION RISK Credit risk is defined as the risk of loss arising from default. Of all the credit analyst roles, rating agency analysts probably adhere most closely to that definition in performing their work. Rating agencies are in the financial information business. They do not trade in financial assets. The function of rating analysts is therefore purely to evaluate, through the assignment of rating grades, the relative credit risk of subject exposures. Traditionally, agency ratings assigned to a given issuer represent, in the aggregate, some combination of probability of default and loss-given-default. However, the fixed income analyst and, on occasion, the counterparty credit analyst, may be concerned with a superficially different form of credit risk that, ironically, can be attributed in part to the rating agencies themselves. The fixed income analyst, especially, is worried not only about the expected loss arising from default, but also about the risk that a companyâ&#x20AC;&#x2122;s bonds, or other debt instruments, may be downgraded by an external rating agency. Although rating agencies ostensibly merely provide an opinion as to the degree of default risk, the very act of providing such assessments tends to have an impact on the market. For example, the downgrade of an issuerâ&#x20AC;&#x2122;s bond rating by one or more external agencies will often result in those bonds having a lower value in the market, even though the actual financial condition of the company and the risk of default may not have altered between the day before the downgrade was announced and the day after. For this reason, this type of credit risk is sometimes distinguished from the credit risk engendered by the possibility of default. It is called downgrade risk, or, more technically, rating migration risk, meaning the risk that the rating of an obligor will change with an adverse effect on the holder of the obligorâ&#x20AC;&#x2122;s securities. At first glance, downgrade risk might be attributed to the role rating agencies play in the market as arbiters of credit risk. But even if no credit rating agencies existed, a risk akin to rating migration risk would exist: the risk of a change in credit quality. Through the flow of information in the market, any significant change in the credit quality of an issuer or counterparty should ultimately manifest in a change in its credit risk assessment made by market participants. At the same time, the changes in perceived creditworthiness would be reflected in market prices implying a change in risk premium commensurate with the price change. Nevertheless, the risk of a decline in credit quality is at the end of the day only of concern insofar as it increases the risk of default. It can therefore be viewed simply as a different manifestation of default risk rather than constituting a discrete form of risk. Nevertheless, rating

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THE BANK CREDIT ANALYSIS HANDBOOK

migration is used in some credit risk models, as it usefully functions as a proxy for changes in the probability of default over time. Ideally, downgrade risk should be equivalent to the risk of a decline in credit quality. In practice, however, there will inevitably be gaps between the rating assigned to a credit exposure and its actual quality as the latter improves or declines incrementally over time. What distinguishes the risk of a decline in credit quality from default risk as conventionally perceived is its impact on securities pricing. A decline in credit quality will almost always be reflected in a widening of spreads above the risk-free rate and a decline in the price of the debt securities affected by the decline. Since price risk by definition constitutes market risk, separating the market risk element from the credit risk element in debt pricing is no easy task.

CATEGORIES OF CREDIT ANALYSIS Until now, we have been looking at the credit decision generally, without reference to the category of borrower. While capacity means having access to the necessary funds to repay a given financial obligation, in practice the evaluation of capacity is undertaken with a view to both the type of borrower and the nature of the financial obligation contemplated. Here the focus is on the category of borrower. Very broadly speaking, credit analysis can be divided into four areas according to borrower type. The four principal categories of borrowers are consumers, nonfinancial companies (corporates), financial companiesâ&#x20AC;&#x201D;of which the most common are banksâ&#x20AC;&#x201D;and government and government-related entities. The four corresponding areas of credit analysis are listed together with a brief description: 1. Consumer credit analysis is the evaluation of the creditworthiness of individual consumers. 2. Corporate credit analysis is the evaluation of nonfinancial companies such as manufacturers, and nonfinancial service providers. 3. Financial institution credit analysis is the evaluation of financial companies including banks and nonbank financial institutions (NBFIs), such as insurance companies and investment funds. 4. Sovereign/municipal credit analysis is the evaluation of the credit risk associated with the financial obligations of nations, subnational governments, and public authorities, as well as the impact of such risks on obligations of nonstate entities operating in specific jurisdictions. While each of these areas of credit assessment shares similarities, there are also significant differences. To analogize to the medical field, surgeons might include orthopedic surgeons, heart surgeons, neurosurgeons, and so on. But you would not necessarily go to an orthopedic surgeon for heart surgery or a heart surgeon for brain surgery. Although the primary subject of this book is the credit analysis of banks, in describing the context in which this specialist activity takes place, it is worth taking a broad look at the entire field.

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The Credit Decision

To begin, let us consider how one might go about evaluating the capacity of an individual to repay his debts, and then briefly consider the same process in reference to both nonfinancial (i.e., corporate)35 and financial companies.

Individual Credit Analysis In the case of individuals, common sense tells us that their wealth, often measured as net worth,36 would almost certainly be an important measure of capacity to repay a financial obligation. Similarly, the amount of incoming cash at an individual’s disposal—either in the form of salary or returns from investments—is plainly a significant attribute as well. Since for most individuals, earnings and cash flow are generally equivalent, income37 and net worth provide the fundamental criteria for measuring their capacity to meet financial obligations.

CONSUMER CREDIT ANALYSIS The comparatively small amounts at risk to individual consumers, broad similarities in the relative structure of their financial statements, the large number of transactions involved, and accompanying availability of data allow consumer credit analysis to be substantially automated through the use of credit-scoring models.

As is our hypothetical Chloe, below, most individuals are employed by businesses or other enterprises, earn a salary and possibly bonuses or commissions, and typically own assets of a similar type, such as a house, a car, and household furnishings. With some exceptions, cash flow as represented by the individual’s salary tends to be fairly regular, as are household expenses. Moreover, unsecured38 credit exposure to individuals by creditors is generally for relatively small amounts. Unsurprisingly, default by consumers is very often the result of loss of income through unemployment or unexpectedly high expenses, as may occur through sudden and severe illness in the absence of health insurance. Because the credit analysis of individuals is usually fairly simple in nature, it is amenable to automation and the use of statistical tools to correlate risk to a fairly limited number of variables. Moreover, because the amounts advanced are comparatively small, it is generally not cost-effective to perform a full credit evaluation encompassing a detailed analysis of financial details and a due diligence interview of the individual concerned. Instead, scoring models that take account of various household characteristics such as salary, duration of employment, amount of debt, and so on, are typically used, particularly with respect to unsecured debt (e.g., credit card obligations). Substantial credit exposure by creditors to individual consumers will ordinarily be in the form of secured borrowing, such as mortgage lending to fund a house purchase or auto finance to fund a car purchase. In these situations, scoring methodologies are also employed, but may be coupled with a modest amount of manual input and review.

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THE BANK CREDIT ANALYSIS HANDBOOK

CASE STUDY: INDIVIDUAL CREDIT ANALYSIS Net worth means an individual’s surplus of assets over debts. Consider, for example, a hypothetical 33-year-old woman named Chloe Williams, who owns a small house on the outskirts of a medium-sized city, let us call it Oakport, worth $140,000.39 There is a remaining mortgage of $100,000 on the house, and Chloe has $10,000 in savings, solely in the form of bank deposits and mutual funds, and no other debts (see Exhibit 1.3). Leaving aside the value of Chloe’s personal property—clothes, jewelry, stereo, computer, motor scooter, for instance—she would have a net worth of $50,000. Chloe’s salary is $36,000 per annum after tax. Since her salary is paid in equal and regular installments in arrears (at the end of the relevant period) on the fifteenth and the last day of each month, we can equate her after-tax income with cash flow. Leaving aside nominal interest and dividend income, her total monthly cash flow (see Exhibit 1.4) would be $3,000 per month.

EXHIBIT 1.3 Chloe's Net Worth Chloe’s Assets

$

Chloe’s House Portion of house value STILL owned to bank 100,000

Portion of house value NOT owned by bank—relatively illiquid Cash and Securities Owns in full without margin loans—liquid assets

40,000

10,000 150,000

Chloe’s Obligations and Equity

$

Remarks: 2-bedroom house at 128 Bayview Drive, Current market value: $140,000

Liabilities— A single major mortgage owed to liability—the funds bank (Chloe’s she owes to the bank, mortgage on her which is an obligation house: financial obligation to bank) 100,000 secured by her house Home equity— unrealized if she 3 sells the house 40,000 7 7 7 7—Chloe’s Net Equity in 7 7 Worth ¼ $50,000 securities— 7 5 unrealized unless she sells them 10,000 150,000

Notes: Value of some assets (house, securities) depends on their market value. Traditionally a business would value them at their fair value or cost.

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The Credit Decision

EXHIBIT 1.4 Chloe's Cash Flow

Chloe’s after-tax income Less: Salary applied to living costs and mortgage payment Net cash flow available to service debt

Annually $

Monthly $

36,000 (26,000)

3,000 (2,167)

10,000

833

Net Cash Flow Net cash flow40 is what remains after taking account of Chloe’s other outgoings: utilities, groceries, mortgage payments, and so on. To analyze Chloe’s capacity to repay additional indebtedness, it is therefore reasonable to consider her net worth and income, together with her net cash flow, her track record in meeting obligations, and her level of job security, among other things. That Chloe has an impeccable credit record, has been with her company, an established Fortune 500 corporation for six years, with a steadily increasing salary and significant net worth would typically be viewed by a bank manager as credit positive.41

Evaluating the Financial Condition of Nonfinancial Companies The process of evaluating the capacity of a firm to meet its financial obligations is similar to that used to assess the capacity of an individual to repay his debts. Generally speaking, however, a business enterprise is more difficult to analyze than an individual. Not only do enterprises vary hugely in the character of their assets, the regularity of their income stream and the degree to which they are subject to demands for cash, but also the financial structure of firms is almost always more complex than it is for individuals. In addition, the interaction of each of the preceding attributes complicates matters. Finally, the amount of funds at stake is usually significantly higher—and not infrequently far higher—for companies than it is for consumers. As a consequence, the credit analysis of nonfinancial companies tends to be more detailed and more hands-on than consumer credit analysis. It is both customary and helpful to divide the credit analysis of organizations according to the attributes to be analyzed. As a paradigm, consider the corporate credit analyst evaluating credit exposure to a nonfinancial firm, whether in the form of financial obligations in the form of bonds issued by multinational firms or breadand-butter loans to be made to an industrial or service enterprise. As a rule, the analyst will be particularly concerned with the following criteria and this will be reflected in the written report that sets forth the conclusions reached: n n n n

The company’s liquidity Its cash flow together with Its near-term earnings capacity and profitability Its solvency or capital position Each of these attributes is relevant also to the analysis of financial companies.

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THE BANK CREDIT ANALYSIS HANDBOOK

Evaluating Financial Companies The elements of credit analysis applicable to banks and other financial companies share many similarities to those applied to nonfinancial enterprises. The attributes of liquidity, solvency, and historical performance mentioned are all highly relevant to financial institutions. As with corporate credit analysis, the quality of management, the state of the economy, and the industry environment are also vital factors in evaluating financial company creditworthiness. Yet, as the business of financial companies differs in fundamental respects from that of nonfinancial businesses, so too does their analysis. These differences have a substantial impact on how the performance and condition of the former are evaluated. Similarly, how various financial characteristics of banks are measured and the weight given to various categories of their performance contrast in many respects with the manner in which corporates are analyzed. Suffice it to say for the moment that the key areas that a credit analyst will focus on in evaluating a bank include the following: n

n

n

n

Earnings capacity—that is, the bank’s performance over time, particularly its ability to generate operating income and net income on a sustained basis and thereby overcome any difficulties it may confront. Liquidity—that is, the bank’s access to cash or cash equivalents to meet current obligations. Capital adequacy (a term frequently used in the context of financial institutions that is essentially equivalent to solvency)—that is, the cushion that the bank’s capital affords it against its liabilities to depositors and the bank’s creditors. Asset quality—that is, the likelihood that the loans the bank has extended to its customers will be repaid, taking into account the value and enforceability of collateral provided by them.

Even in this brief list, differences between the key criteria applied to corporate credit analysis and those important in the credit analysis of financial and nonfinancial companies are apparent. They are: n n

The importance of asset quality. The omission of cash flow as a key indicator.

We will discuss these differences, as well as the reasons for them, in succeeding chapters. As with nonfinancial companies, qualitative analysis plays a substantial role, even a more important role, in financial institution credit analysis. Finally, it should be noted that there is a great deal of diversity among the entities that comprise the financial sector. In this book, we focus almost exclusively on banks. They are the most important category of financial institutions and also probably the most numerous. Banking organizations, so defined, nonetheless embrace a wide range of institutions, and the category embraces a number of subcategories, including commercial banks, specialized, wholesale banks, trust banks, development banks, and so on. The number of categories present within a particular country’s financial sector depends upon the structure of the industry and the applicable laws governing

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The Credit Decision

it. Equally, the terminology used to refer to the different categories of banking institutions is no less diverse, with the relevant statutory definitions for each type varying to a greater or lesser extent from jurisdiction to jurisdiction. Aside from banks, the remainder of the financial sector is composed of a variety of other types of entities including insurance companies, securities brokerages, and asset managers. Collectively, these entities are referred to as nonbank financial institutions, or as NBFIs. As with the banking industry, the specific composition of the NBFI sector in a particular jurisdiction is influenced by applicable laws, regulations, and government policy. In these pages, we will focus almost exclusively on commercial banks. An in-depth discussion of the credit analysis of NBFIs is really the subject for another book.

A QUANTITATIVE MEASUREMENT OF CREDIT RISK So far, our inquiry into the meaning of credit has remained within the confines of tradition. Credit risk has been defined as the likelihood that a borrower will perform a financial obligation according to its terms; or conversely, the probability that it will default on that commitment. The probability that a borrower will default on its obligation to the lender generally equates to the probability that the lender will suffer a loss. As so defined, credit risk and default risk are essentially synonymous. While this has long been a serviceable definition of creditworthiness, developments in the financial services industry and changes in regulation of the sector over the past decade have compelled market participants to revisit the concept. Probability of Default If we think more about the relationship between credit risk and default risk, it becomes apparent that such probability of default (PD), while highly relevant to the question what constitutes a “good credit”42 and what identifies a bad one, is not the creditor’s only, or in some cases even her central concern. Indeed, a default could occur, but should a borrower through its earnest efforts rectify matters promptly— making good on the late payment through the remittance of interest or penalty charges—and resume performance without further breach of the lending agreement, the lender would be made whole and suffer little harm. Certainly, nonpayment for a brief period could cause the lender severe consequential liquidity problems, should it have been relying upon payment to satisfy its own financial obligations, but otherwise the tangible harm would be negligible. Putting aside for a moment the impact of default on a lender’s own liquidity, if mere default by a borrower alone is not what truly concerns a creditor, about what then is it really worried? Loss Given Default In addition to the probability of default, the creditor is, or arguably should be, equally concerned with the severity of the default that might be incurred. It is perhaps easier to comprehend retrospectively. Was it a brief, albeit material default, like that described in the preceding paragraph, that was immediately corrected so that the creditor obtained all the expected benefits of the transaction?

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THE BANK CREDIT ANALYSIS HANDBOOK

Or was it the type of default in which payment ceases and no further revenue is ever seen by the creditor, resulting in a substantial loss as a result of the transaction? Clearly, all other things being equal, it is the expectation of the latter that most worries the lender. Both the probability of default and the severity of the loss resulting in the event of default—each of which is conventionally expressed in percentage terms— are crucial in ascertaining the tangible expected loss to the creditor, not to speak of the creditor’s justifiable level of apprehension. The loss given default (LGD) encapsulates the likely percentage impact, under default, on the creditor’s exposure. Exposure at Default The third variable that must be considered is exposure at default (EAD). EAD may be expressed either in percentage of the nominal amount of the loan (or the limit on a line of a credit) or in absolute terms. Expected Loss The three variables—PD, LGD, and EAD—when multiplied, give us expected loss for a given time horizon.43 It is apparent that all three variables are quite easy to calculate after the fact. Examining its entire portfolio over a one-year period, a bank may determine that the PD, adjusted for the size of the exposure, was 5 percent, its historical LGD was 70 percent, and EAD was 80 percent of the potential exposure. Leaving out asset correlations within the loan portfolio and other complexities, expected loss (EL) is simply the product of PD, LGD, and EAD. EL and its constituents are, however, much more difficult to estimate in advance, although past experience may provide some guidance. The Time Horizon All the foregoing factors are time dependent. The longer the tenor44 of the loan, the more likely it is that a default will occur. EAD and LGD will also change with time, the former increasing as the loan is fully drawn, and decreasing as it is gradually repaid. Similarly, LGD can change over time, depending upon the specific terms of the loan. The nature of the change depends upon the specific terms and structure of the obligation. Application of the Concept To summarize, expected loss is fundamentally dependent upon four variables, with the period often assumed to be one year for the purposes of comparison and analysis. On a portfolio basis, a fifth variable, correlation between credit exposures within a credit portfolio, will also affect expected loss. The PD/LGD/EAD concept just described is extremely valuable as a way to understand and model credit risk. It will be developed at length in subsequent chapters.

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The Credit Decision

25

Major Bank Failure Is Relatively Rare While bank credit analysis resembles corporate credit analysis in many respects, it differs in several important ways. The most crucial difference is that, broadly speaking, modern banks, in sharp contrast to nonfinancial firms, do not fail in normal times. That may seem like a shocking statement. It is an exaggeration, but one that has more truth in it than might first appear, considering that, quite often, weak banks are conveniently merged into other—supposedly stronger—banks. Most bank analysts, if you press them hard enough, will acknowledge the declaration as generally valid, when applied to the more prominent and internationally active institutions that are the subject of the vast majority of credit analyses. Granted, the present time, in the midst of a substantial financial crisis, does not qualify as normal time. In each of 2009 and 2010, roughly 2 percent of U.S. banks failed, and in 2011, so did roughly 1.2 percent of them. The rate of failure between 1935 and 1940 was about 0.5 percent per year, and it remained below 0.1 percent per year in the 20 years after World War II. Between 2001 and 2008, only 50 banks failed in the United States—half of them in 2008 alone, but that left the overall ratio of that period below 0.1 percent per year. In the United States alone, other data show that the volume of failures of publicly traded companies numbered in the thousands, with total business bankruptcies in the millions. To be sure, the universe of banks is much smaller than that of nonfinancial companies, but other data confirms that bank collapses are substantially less probable than those of nonfinancial enterprises. This is, of course, not to say that banks never fail (recall the foregoing qualification, “broadly speaking”). It is evident the economic history of the past several centuries is littered with the invisible detritus of many long-forgotten banks. Small local and provincial banks, as well as—mostly in emerging markets— sometimes larger institutions are routinely closed by regulators, or merged or liquidated, or taken over by other healthier institutions, without creating systemic waves. The proportion of larger banks going into trouble has dramatically increased in the past few years, particularly in the UK and in the United States, but also in Europe. The notion of too big to fail has always been accepted in the context of each separate market. In November 2011, that notion was extended to include a systemic risk of contagion, with the publication by the Financial Stability Board (FSB) of a list of 29 “systemically important financial institutions” which would be required to hold “additional loss absorption capacity tailored to the impact of their [possible] default.” There are of course many more “too big to fail” financial institutions around the world. The notion of “too small to fail” also exists since it is often cheaper and more expedient—not to mention less embarrassing—for governments to arrange the quiet absorption of a small bank in trouble. A wide danger zone remains in between those two extremes. A thorough discussion about default can be found in later chapters of this book.

Bank Insolvency Is Not Bank Failure The proposition that banks do not fail is, it must be emphasized, an overstatement meant to illustrate a general rule. There is no intent to convey the notion that banks

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THE BANK CREDIT ANALYSIS HANDBOOK

do not become insolvent, for especially with regard to banks (as opposed to ordinary corporations), insolvency and failure are two distinct events. In fact, bank insolvency is far more common, even in the twenty-first century, than many readers are likely to suspect.45 Equally, insolvent banks can keep going on and on like a notorious advertising icon so long as they have a source of liquidity, such as a central bank as a lender of last resort. What is meant is that the bankruptcy or collapse of a major commercial banking institution that actually results in a significant loss to depositors or creditors is an extremely rare event.46 Or at least it did remain so until the crisis that started in 2008. For the vast majority of institutions that a bank credit analyst is likely to review, a failure is highly improbable. But because banks are so highly leveraged, these risks, and perhaps more importantly, the risks that episodes of distress that fall short of failure and may potentially cause harm to investors and counterparties, are of such magnitude that they cannot be ignored.

Why Bother Performing a Credit Evaluation? If major bank failures are so rare, why bother performing a credit evaluation? There are several reasons. n

n

n

n

First, evaluating the default risk of an exposure to a particular institution enables the counterparty credit analyst working for a bank to place the risk on a rating scale, which helps in pricing that risk and allocating bank capital. Second, even though the risk of default is low, the possibility is a worrisome one to those with credit exposure to such an institution. Consequently, entities with such exposure, including nonfinancial and nonbank financial organizations, as well as investors, both institutional and individuals, have an interest in avoiding default-prone institutions.47 Third, it is not only outright failure that is of concern, but also events short of default can cause harm to counterparties and investors. Fourth, globalization has increased the risk of systemic contagion. As a result, the risk on a bank has becomes a twice-remote risk—or in fact a risk compounded many times—on that bank’s own risk on other financial institutions with their own risk profile.

Default as a Benchmark That bank defaults are rare—barring systemic crises—in today’s financial industry does not detract from the conceptual usefulness of the possibility of default in delineating a continuum of risk.48 The analyst’s role is to place the bank under review somewhere on that continuum, taking account of where the subject institution stands in terms of financial strength and the potential for external support. The heaven of pure creditworthiness49 and the hell of bankruptcy50 define two poles, somewhere between which a credit evaluation will place the institution in terms of estimated risk of loss. In terms of credit ratings, these poles are roughly demarcated by an AAA rating at one end, and a default rating at the other. In other words, the potential for failure, if not much more than a remote possibility in most markets, nonetheless allows us to create a sensible definition of credit risk and a spectrum of expected loss probabilities in the form of credit ratings.

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27

The Credit Decision

In turn, these ratings facilitate the external pricing of bank debt and, internally, the allocation of bank capital. Pricing of Bank Debt From a debt investor’s perspective, the assessment of bank credit risk distilled into an internal or external rating facilitates the determination of an investment’s value, that is, the relationship of risk and reward, and concomitantly its pricing. For example, if hypothetical Dahlia Bank and Fuchsia Bank, both based in the same country, each issue five-year subordinated floating-rate notes paying a semiannual coupon of 6 percent, which is the better investment? Without additional information, they are equally desirable. However, if Dahlia Bank is a weaker credit than Fuchsia Bank, then all other things being equal, Fuchsia Bank is likely to be the better investment since it offers the same rate of return for less risk to the investor. These evaluations of bank obligations and the information they convey to market participants function to underpin the development and maintenance of efficient money and capital markets. The relationship between credit risk and the return that investors will require to compensate for increasing risk levels can be depicted in a rating yield curve. The diagram in Exhibit 1.5 illustrates a portion of such a curve. Credit risk, as reflected in assigned credit ratings, is shown on the horizontal axis, while the risk premium, described as basis points above the risk-free rate demanded by investors, is shown on the vertical axis. Observe that as of the time captured, for a financial instrument having a rating of BBB, corresponding roughly to a default probability within one year of between 0.2 percent and 0.4 percent,51 investors required a premium of about 200 basis points (bps) or 2 percent yield above the riskfree rate. 1,400

Basis Points over U.S. Treasuries

1,200

1,000

800

600

AAA/Aaa

1

AA/Aa1

2

AA/Aa2

3

AA–/Aa3

4

Based upon the rating yield curve shown, investors demanded a risk premium of about 200 bp (2%) over the market yield for U.S. Treasuries for a debt issue rated BBB/Baa2.*

A/A1

5

*Numerical equivalent of 9 as per table.

A/A2

6

A–/A3

7

BBB/Baa1

8

BBB/Baa2

9

BBB–/Baa3

10

400

200

0

0

2

4

6

8

9

10

12

14

16

18

20

Average of Moody’s and S&P Ratings, Numerical Equivalent

EXHIBIT 1.5 External Ratings and the Rating Yield Curve

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THE BANK CREDIT ANALYSIS HANDBOOK

Allocation of Bank Capital From the counterparty credit analyst’s perspective, the same assessment process enables the institution for which she works to better allocate its risks and its capital in a manner that is both prudent and compliant with relevant regulatory prescriptions.52 For internal risk management purposes, bank analysis facilitates the setting of exposure limits with regard to the advance of funds, exposure to derivatives, and settlement. Events Short of Default The default of an entity to which one has credit exposure is obviously something to be avoided. The risk of default is also useful conceptually to define a spectrum of default risk. But what about events short of default? How do they figure in the calculus of the creditor or investor? As alluded to, a bank does not have to fail for it to cause damage to a counterparty or creditor. A technical default, not to speak of a more material one, can have critical consequences. If a company’s treasurer, for example, loses access to funds on deposit with a bank even temporarily, this loss of access can have serious knock-on effects, even if all the funds at stake are ultimately repaid.53 Likewise, if one bank is relying upon another’s creditworthiness as part of a larger transaction, the first bank’s default, again even if only technical or temporary, can be a grave matter for its counterparty, potentially harming its own credit rating and reputation in the market. In all of the foregoing cases, irrespective of the likelihood of outright bank failure, bank credit analysis provides the means to avoid fragile banks, as well as the tools to steer clear of failure-prone institutions in markets where bank collapses are not so uncommon.

Banks Are Different Banks are different in that they are highly regulated and their assessment is intrinsically highly qualitative. In many respects, however, bank credit analysis and corporate credit analysis are more alike than they are dissimilar. Yet there are vital differences in their respective natures that call for separate approaches to their evaluation both in respect to the qualitative and quantitative aspects of credit analysis. Some of these differences relate to the structure of a bank’s financial statements as compared with a nonfinancial entity. Others have to do with the role of banks within a jurisdiction’s financial system and their impact on the local economy. The banking sector is among the most tightly regulated of all industries worldwide. This fact alone means that the scope, character, and effectiveness of the regulatory apparatus will inevitably affect the performance and financial condition of institutions that come within its ambit. Consequently, consideration of the adverse and beneficial consequences of existing regulations, and proposed or promulgated changes, will necessarily assume a higher profile than is normally the case in connection with nonfinancial companies. The reason that banks are heavily regulated is in large part attributable to the preeminent role they play within the financial markets in which they operate. As crucial components within a national payment system and the extension of credit within a region or country, their actions and health inevitably have a major impact

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The Credit Decision

29

on the climate of the surrounding economy. Consequently, banks are more important than their apparent size, measured in terms of revenue generation and employment levels, would suggest. It should not be surprising therefore that governments around the world take a keen interest in the health of the banks operating within their borders, and, supervise them to a far greater degree than they do nonfinancial enterprises. In contrast, nonfinancial firms, with a few exceptions, are lightly regulated in most jurisdictions, and governments generally take a hands-off policy toward their activities. In most contemporary market-driven economies, if an ordinary company fails, it is of no great concern. This is not so in the case of banks. Because they depend on depositor confidence for their survival, and since governments neither want to confront irate depositors, nor more critically, contend with a significant number of banks unable to function as payment and credit conduits, deposit-taking institutions are rarely left to fend for themselves and go bust without a passing thought. Even where deposit insurance exists and depositors remain pacified, the failure of a single critical financial institution may be plausibly viewed by policymakers as likely to have a detrimental impact on the health of the regional or national financial system. Moreover, the costs of repairing a banking crisis typically far outweigh the costs of taking prudent measures to prevent one. Governments therefore actively monitor, regulate, and—in light of the importance of banks to their respective economies— ultimately function as lenders of last resort through the national central bank, or an equivalent agency. Owing to the privileged position that banks commonly enjoy, their credit analysis must give due consideration to an institution’s role within the relevant financial system. Its position will affect the analyst’s assessment concerning the probability, and degree, of support that may be offered by the state—whether explicitly or more commonly implicitly—in the case the bank experiences financial distress. Making such assessments not only calls for consideration of applicable laws and regulations, but also relevant institutional structures and policies, both historic and prospective. Moreover, the analysis must consider policies that, in an effort on the part of government to reduce moral hazard,54 may be quite opaque. In sum, this aspect of the analytical process necessarily requires keen judgment, and is in consequence principally qualitative in character.55

NOTES 1. Walter Bagehot, Lombard Street: A Description of the Money Market (1873), hereafter Lombard Street. Bagehot (pronounced “badget” to rhyme with “gadget”) was a nineteenth-century British journalist, trained in the law, who wrote extensively about economic and financial matters. An early editor of The Economist, Bagehot’s Lombard Street was a landmark financial treatise published four years before his death in 1877. 2. Various attributions; see for example Global-Investor.com; 500 of the Most Witty, Acerbic and Erudite Things Ever Said About Money (Harriman House, 2002). Author of Adventure Capitalist and Investment Biker, Jim Rogers is best known as one of the world’s foremost investors. As co-founder of the Quantum fund with George Soros in 1970, Rogers’s extraordinary success as an investor enabled him to retire at the age of 37. He remains in the public eye, however, through his books and commentary in the financial media.

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THE BANK CREDIT ANALYSIS HANDBOOK

3. See, for example, “credit. . . . Etymology: Middle French, from Old Italian credito, from Latin creditum, something entrusted to another, loan, from neuter of creditus, past participle of credere, to believe, entrust.” Merriam-Webster Online Dictionary, www.m-w .com. Webster’s Revised Unabridged Dictionary (1913) defines the term to mean: “trust given or received; expectation of future payment for property transferred, or of fulfillment or promises given; mercantile reputation entitling one to be trusted; applied to individuals, corporations, communities, or nations; as, to buy goods on credit.” www.dictionary.net/ credit. Walter Bagehot, whose quoted remarks led this chapter, gave the meaning of the term as follows: “Credit means that a certain confidence is given, and a certain trust reposed. Is that trust justified? And is that confidence wise? These are the cardinal questions. To put it more simply, credit is a set of promises to pay; will those promises be kept?” (Bagehot, Lombard Street). 4. This is assuming, of course, that the financial condition of the borrower has been honestly and openly represented to the creditor through the borrower’s financial statements. The relevance of the assumption remains important, as the discussions concerning financial quality later in the book illustrate. 5. As put by John Locke, the seventeenth-century British philosopher, “credit [is] nothing but the expectation of money within some limited time . . . money must be had, or credit will fail.”—vol. 4 of The Works of John Locke in Nine Volumes, 12th ed. (London: Rivington, 1824), www.econlib.org. Credit exposure (exposure to credit risk) can also arise indirectly as a result of a transaction that does not have the character of loan, such as in the settlement of a securities transaction. The resultant settlement risk is a subset of credit risk. Aside from settlement risk, however, credit risk implies the existence of a financial obligation, either present or prospective. 6. R. Taggart Murphy, The Real Price of Japanese Money (London: Weidenfeld & Nicolson, 1996), 49. Murphy’s book was published in the United States as The Weight of the Yen: How Denial Imperils America’s Future and Ruins an Alliance (New York: W. W. Norton, 1996). Although Taggart’s book is primarily concerned with the U.S.–Japan trade relationship as it evolved during the post–World War II period, Chapter 2 of the text, entitled “The Credit Decision,” provides an instructive sketch of the function of credit assessment in the commercial banking industry. 7. The phrase “on a commercial basis” is used here to mean in an “arm’s-length” business dealing with the object of making a commercial profit, in contrast to a transaction entered into because of friendship, family ties, or dedication to a cause, or as a result of any other noncommercial motivation. 8. There are four basic types of collateral: (1) real or personal property (including inventory, trade goods, and intangible property); (2) negotiable instruments (including securities); (3) other financial collateral (i.e., other financial assets); and (4) floating charges on business assets. Current assets refers to assets readily convertible to cash. These are also known as liquid assets. 9. Foreclosure is a legal procedure to enforce a creditor’s rights with respect to collateral pledged by a delinquent borrower that enables the creditor to legally retain or to sell the collateral in full or partial satisfaction of the debt. 10. Bankruptcy is the legal status of being insolvent or unable to pay debts. Bankruptcy proceedings are legal proceedings in which a bankruptcy court or similar tribunal takes over the assets of the debtor and appoints a receiver or trustee to administer them. Unsecured creditors may also be able to initiate bankruptcy proceedings, but are less sure of compensation than the secured creditor. 11. A related question is whether the legal framework is sufficiently robust to enable the creditor to enforce his rights against the borrower. Where creditors’ rights are weak or difficult to enforce, this consideration becomes part of the credit decision-making process.

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12. Roger H. Hale, Credit Analysis: A Complete Guide (New York: John Wiley & Son’s 1983, 1989). The traditional reliance on collateral has given rise to the term “pawnshop mentality” to refer to bankers who are incapable or unwilling to perform credit analysis of their customers and lend primarily on the value of collateral pledged. See for example Szu-yin Ho and Jih-chu Lee, The Political Economy of Local Banking in Taiwan (Taipei, Taiwan: NPF Research Report, National Policy Foundation, December 10, 2001). “Because of the pawnshop mentality and practices in banking institutions, the SMEs are not considered good customers for loans,” www.npf.org.tw/English/Publication/FM/FMR-090-069.htm. SME is an acronym for small- and medium-size enterprise. Murphy, referring to banking practice in Japan in the 1980s, observed that “Japanese banks rarely extend domestic loans of any but the shortest maturity without collateral” (Real Price, 49). 13. Typically, in this situation, the loan would be advanced by an auto manufacturer’s finance subsidiary. 14. Financial analysis is the process of arriving at conclusions concerning an entity’s financial condition or performance through the examination of its financial statements, such as its balance sheet and income statement. Financial analysis encompasses a wide range of activities that may be employed for internal management purposes (e.g., to determine which business lines are most profitable) or for external evaluation purposes (e.g., equity or credit analysis). 15. Joint and several liability is a legal concept under which each of the parties to an obligation is liable to the full extent of the amount outstanding. In other words, where there are multiple obligors, the obligee (creditor) is entitled to demand full repayment of the entire outstanding obligation from any and all of the obligors (borrowers). 16. As well as having an impact on whether to advance funds, the use of collateral, guarantees, and other credit risk mitigants may also serve to increase the amount of funds the lender is willing to put at risk. 17. Repo finance refers to the use of repurchase agreements and reverse repurchase agreements to facilitate mainly short-term collateralized borrowings and advances. Securities lending transactions are similar to repo transactions. Both are explained in more detail in Chapter 3. 18. Free will has been defined as the power of making choices unconstrained by external agencies, wordnet.princeton.edu/perl/webwn. If so defined—that is, meaning having the freedom to choose a course of action in the moment—it is by definition not predetermined, and therefore the actions of an entity having free will cannot be 100 percent predictable. This is not to say, however, that predictive—if not determinative—factors cannot be identified, and that the probability of various scenarios unfolding cannot be estimated, especially when the number of transactions involved is large. Indeed, much of credit risk evaluation is underpinned by implicit or explicit statistical expectations based on the occurrence of a large number of transactions. 19. Due diligence means the review of accounts, documentation, and related written materials, together with interviews with an entity’s principals and key personnel, for the purpose of supporting a professional assessment concerning the entity. A due diligence investigation is typically performed in connection with a prospective transaction. So a law firm would likely perform a due diligence investigation before rendering of a legal opinion concerning an anticipated transaction. So, too, will a rating agency undertake a due diligence review before assigning a rating to an issuer. 20. A correspondent bank is a bank that has a relationship with a foreign banking institution for which it performs services in the correspondent bank’s home market. Since few, if any banks, can feasibly maintain branches in all countries of the world, correspondent banking relationships enable institutions without branches or offices in a given jurisdiction to act

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32

21.

22.

23.

24.

25.

26.

27.

THE BANK CREDIT ANALYSIS HANDBOOK on a global basis on behalf of such institutions’ clients. Typical services provided by a correspondent bank for a foreign institution include check clearing, funds transfers, and the settlement of transactions, acting as a deposit or collection agent for the foreign bank, and participating in documentary letter of credit transactions. Lombard Street, note 2 supra, quoted in Martin Mayer, The Bankers: The Next Generation (Penguin, 1996), 10. The quotation comes from Chapter 3 of the book, entitled “How Lombard Street Came to Exist, and Why It Assumed Its Present Form,” and the passage discusses the evolution of commercial banks from institutions reliant on noteissuance to those dependent upon the acceptance of deposits. Note that a leading textbook on bank management also pays homage to the axiom that bankers understand their own geographic franchise best and “are more apt to misjudge the quality of loans originating outside [it]. . . . [and] loan officers will be less alert to the economic deterioration of communities outside their trade areas.” George H. Hempel, Donald G. Simonson, and A. Coleman, Bank Management: Text and Cases, 4th ed. (hereafter Bank Management) (New York: John Wiley & Sons, 1994), 377. For example, Bank Management, note 21 supra, observes that there is a consensus among bankers that “the paramount factor in a successful loan is the honesty and goodwill of the borrower,” and rates a borrower’s character as one of the four fundamental credit criteria to be considered, together with the purpose of the funds, and the primary and secondary sources of loan repayment. In a specialist book focused on emerging markets, character is one of five “Cs” of credit, along with capacity, capital, collateral, and conditions. Waymond A. Grier, The Asiamoney Guide to Credit Analysis in Emerging Markets (Hong Kong: Asia Law & Practice, 1995), 11. Ironically, name lending is also called “character lending.” Distinct from this phenomenon is related-party lending, which means advancing funds to a family member of a bank owner or officer, or to another with whom the owner or officer has a personal or business relationship separate from those arising from his or her capacity as a shareholder or as an employee of the bank. It should be borne in mind that whether relying on first-hand knowledge, reputation, or the borrower’s credit history, the analytical distinction between willingness to pay and capacity to pay is easily blurred. In discussing character, Bank Management distinguishes among fraudulent intent, moral failings, and other deficiencies, such as lack of intelligence or management skills, some of which might just as easily come under the heading of management assessment. Ultimately, the creditor is only concerned whether the borrower is good for the funds “entrusted” to him, and as a practical matter there is little to be gained for this purpose in attempting to parse between how much this belief rests on willingness to pay and how much it rests upon capacity to pay. Where, however, there exist a large number of recorded transactions, stronger correlations may be drawn between the borrower’s track record and future behavior, allowing the probability of default to be better predicted. This, of course, increases a bank’s ability to manage risk, as compared with other entities that engage in comparatively few credit transactions and provides banks with an essential competitive advantage in this regard. The value of such experience comes not only from the bank officer’s having reviewed a greater variety of credit exposures, but also from having gone through an entire business cycle and seen each of its phases and corresponding conditions. As with the credit analysis of large corporate entities, in the credit analysis of (rather than by) financial institutions, the criterion of character tends to be given somewhat less emphasis than is the case in respect to individuals and small businesses. As various financial scandals have shown, however, this reduced emphasis on character is not necessarily justified. The term moral obligation is used here to distinguish it from a legally enforceable obligation. A legal obligation may also represent a moral obligation, but a moral obligation does not necessarily give rise to a legal one.

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28. While full recovery may nonetheless still be impossible, depending upon the borrower’s access to funds and the worth of the collateral securing the loan, partial recovery is generally more likely when creditors’ rights are strong. The effectiveness of a legal system encompasses many facets, including the cost and time required to obtain legal redress, the consistency and fairness of legal decisions, and the ability to enforce judicial decisions rendered. It may be added that the development of local credit reporting systems also may affect a borrower’s willingness to fulfill financial obligations since a borrower may wish to avoid the detrimental effects associated with being tagged as a less than prime credit. 29. In this book, the terms and phrases such as legal efficiency and quality of the legal infrastructure are used more or less synonymously to refer to the ability of a legal system to enforce property rights and creditors’ rights fairly and reliably, as well as in a reasonably timely and cost-effective manner. In a scholarly context, these terms are also used to refer to the ability of legal institutions to reduce “idiosyncratic risk” to entrepreneurs and to prevent the exploitation of outside investors from insiders by protecting their property rights in respect of invested funds. See for example Luc Laeven, “The Quality of the Legal System, Firm Ownership, and Firm Size,” presentation at the Stanford Center for International Development, Stanford University, Palo Alto, California, November 11, 2004. 30. It is apparent that there is almost always a risk that a credit transaction favoring the creditor may not be enforced. This risk is often subsumed under the broader rubric of legal risk. Legal risk may be defined generally as a category of operational risk or event risk that may arise from a variety of causes, which insofar as it affects credit risk becomes a proper concern of the credit analyst. Types of legal risk include (1) an adverse change in law or regulation; (2) the risk of being a defendant in time-consuming or costly litigation; (3) the risk of an arbitrary, discriminatory, or unexpected adverse legal or regulatory decision; (4) the risk that the bank’s rights as creditor will not be effectively enforced; (5) the risk of ineffective bank supervision; or (6) the risk of penalties or adverse consequences incurred as a result of inadvertent errors in documentation. Note that these subcategories are not necessarily discrete, and may overlap with each other or with other risk classifications. 31. Regrettably for lenders in emerging markets, effective protection of creditors’ rights is not the norm. As was seen in the aftermath of the Asian financial crisis during 1997– 1998, the legal systems in some countries were demonstrably lacking in this regard. Reforms that have been implemented, such as the revised bankruptcy law enacted in Thailand in 1999, have gone some distance toward remedying these deficiencies. The efficacy of new statutes is, however, dependent upon a host of factors, including the attitudes, expertise, and experience of all participants in the judicial process. In Thailand and Indonesia, as well as in other comparable jurisdictions where legal reforms have been implemented, it can be expected that it will take some years before changes are thoroughly manifested at the day-to-day level. Similarly, the debt moratorium and emergency laws enacted in Argentina in 2001 and 2002 curtailed creditors’ rights in a substantial way. Incidentally, in June 2010 in Iceland, not exactly an emerging market, the Supreme Court ruled that some loans indexed to foreign currency rates were illegal, shifting the currency losses from borrowers to lenders. Similar decisions may yet be taken in Hungary or in Greece. 32. Coined in 1981 by Antoine W. van Agtmael, an employee of the International Finance Corporation, an affiliate of the World Bank, the term emerging market is broadly synonymous with the terms less-developed country (LDC) or developing country, but generally has a more positive connotation suggesting that the country is taking steps to reform its economy and increase growth with aspirations of joining the world’s developed nations (i.e., those characterized by high levels of per capita income among various relevant indicia). Leading emerging markets at present include, among others, the following

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34

33. 34.

35.

36.

37.

38. 39.

40. 41.

42. 43. 44. 45. 46.

47.

THE BANK CREDIT ANALYSIS HANDBOOK countries: China, India, Malaysia, Indonesia, Turkey, Mexico, Brazil, Chile, Thailand, Russia, Poland, the Czech Republic, Egypt, and South Africa. Somewhat more developed countries, such as South Korea, are sometimes referred to as NICs, or newly industrialized countries. Somewhat less-developed countries are sometimes referred to anecdotally as subemerging markets, a term that is somewhat pejorative in character. This is an illustration of the problem of moral hazard that is discussed later in the book. The term financial company is used here to contrast financial intermediaries with nonfinancial enterprises. Not to be confused with the term finance company, financial company refers to banks as well as to nonbank financial intermediaries, abbreviated NBFIs. The concept of intermediation is explored further in Chapter 3. While banks and other financial institutions are usually organized as corporations, the term corporate is frequently used both as an adjective and as a noun to generically refer to nonfinancial enterprises, such as manufacturers, wholesalers, and retailers, electrical utilities and service providers, owned by mainly private investors as opposed to those principally owned or controlled by governments or their agencies. The latter would usually fall under the rubric of state-owned enterprises. Net worth may be defined as being equal to assets less liabilities, and is generally synonymous with the following terms which are often used to describe the same concept in relation to companies: equity capital; total equity; net assets; owners’ equity; stockholders’ equity; shareholders’ funds; net asset value; and net tangible assets (net assets less intangible assets such as goodwill). More generally, it may be observed that financial terms are often associated with a plethora of synonyms, while, at the same time, fundamental terms such as capital or nonperforming loans may have distinctly different meanings depending upon the circumstances. Income is an accounting concept distinct from cash flow in that it seeks to match past and future cash flows with the transaction that generated them, rather than classifying such movements strictly on the basis of when—that is, in which financial reporting period— they occurred. The differences between income and cash flow are discussed in Chapter 6. Unsecured means without security such as collateral or guarantees. Note that for an individual, net worth is frequently calculated taking account of the market value of key assets such as real property, in contrast to company credit analysis, which, with some exceptions, will value assets at their historical cost. Net cash flow can be defined as cash received less cash paid out for a given financial reporting period. Credit positive means tending to strengthen an entity’s perceived creditworthiness, while credit negative suggests the opposite. For example, “[The firm’s] recent disposal of the fiber-optic network is slightly credit positive.” Ivan Lee et al., Asia-Pacific–Fixed Income, Asian Debt Perspective–Outlook for 2002 (Hong Kong: Salomon Smith Barney, January–February 2002), 24. A good credit means, of course, a good credit risk; that is, a credit risk where the risk of loss is so minimally low as to be acceptable. For simplicity’s sake, variables such as the period and correlations within a loan portfolio are omitted in this introductory discussion. Tenor means the term or time to maturity of a credit instrument. Moreover, not all episodes of bank distress reach the newspapers, as problems are remedied by regulators behind the scenes. In short, while historically a sizable number of banks have closed their doors, and bank defaults and failures are not unknown even today. The types of institutions that do suffer bank collapses are almost always local or provincial, with few, if any, international counterparty relationships. Retail depositors will also be concerned with a bank’s possible default, although their deposits are often insured by governmental or industry entities to some extent.

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48. As discussed, credit risk is largely measured in terms of the probability of default, and loss severity (loss given default). 49. In other words, 0 percent risk of loss, that is, a risk-free investment. 50. The worst-case scenario refers to an institution in default, subject to liquidation proceedings, in which 100 percent of principal and interest are unrecoverable. 51. Such default risk ranges are associated with ratings. In the past, each rating agency defined ratings in vague terms rather than as probabilities of default—other than as ranges of historical observations—and each rating agency would have its own definition. The advent of Basel II has now forced them to map each rating level to a range of probabilities of default. 52. A discussion of the mechanics of economic capital allocation is unnecessary for the purposes of this chapter, but it will of course be developed later in the book. 53. A decline in the credit quality of the bank has prompted the analyst to seek a reduction in limits for the exposure to the bank. 54. Industrial and service companies are thus far less likely to benefit from a government bailout, although this likelihood depends on the political–economic system. Even in highly capitalist countries, there are exceptions where the firm is very large, strategically important, or politically influential. The bailout of automaker Chrysler Corporation in the United States, a company that was later acquired by Daimler-Benz, was a notable illustration. More recently, the 2008 crisis prompted substantial government intervention in Europe and in the United States. In more dirigiste economies, state bailouts are less rare. Still, even in these economies, most corporates have no real lender of last resort, and must remain solvent and liquid on pain of fatal collapse. Perhaps, in consequence, their quantitative performance and solvency indicia tend to be scrutinized more severely than those of their financial institution counterparts. 55. Aside from their relevance in such extreme circumstances, because of the degree to which bank performance is affected by government regulation and supervision, the same considerations are important in the ongoing analysis of a bank’s financial condition.

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Contents

Preface to the Second Edition

xiii

Acknowledgments

xvii

PArt ONE

the Quant Universe ChAPtEr 1 Why Does Quant trading Matter?

The Benefit of Deep Thought The Measurement and Mismeasurement of Risk Disciplined Implementation Summary Notes

ChAPtEr 2 An Introduction to Quantitative trading

What Is a Quant? What Is the Typical Structure of a Quantitative Trading System? Summary Notes

3

8 9 10 11 11

13

14 16 19 20

PArt tWO

Inside the Black Box ChAPtEr 3 Alpha Models: how Quants Make Money

Types of Alpha Models: Theory-Driven and Data-Driven Theory-Driven Alpha Models Data-Driven Alpha Models Implementing the Strategies

23

24 26 42 45

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x

Contents

Blending Alpha Models Summary Notes

56 62 64

ChAPtEr 4 risk Models

Limiting the Amount of Risk Limiting the Types of Risk Summary Notes

ChAPtEr 5 transaction Cost Models

Defining Transaction Costs Types of Transaction Cost Models Summary Note

ChAPtEr 6 Portfolio Construction Models

Rule-Based Portfolio Construction Models Portfolio Optimizers Output of Portfolio Construction Models How Quants Choose a Portfolio Construction Model Summary Notes

ChAPtEr 7 Execution

Order Execution Algorithms Trading Infrastructure Summary Notes

ChAPtEr 8 Data

The Importance of Data Types of Data Sources of Data Cleaning Data Storing Data Summary Notes

67

69 72 76 78

79

80 85 90 91

93

94 98 112 113 113 115

117

119 128 130 131

133

133 135 137 139 144 145 146

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Contents

ChAPtEr 9 research

Blueprint for Research: The Scientific Method Idea Generation Testing Summary Note

147

147 149 151 170 171

PArt thrEE

A Practical Guide for Investors in Quantitative Strategies ChAPtEr 10 risks Inherent to Quant Strategies

Model Risk Regime Change Risk Exogenous Shock Risk Contagion, or Common Investor, Risk How Quants Monitor Risk Summary Notes

ChAPtEr 11 Criticisms of Quant trading

Trading Is an Art, Not a Science Quants Cause More Market Volatility by Underestimating Risk Quants Cannot Handle Unusual Events or Rapid Changes in Market Conditions Quants Are All the Same Only a Few Large Quants Can Thrive in the Long Run Quants Are Guilty of Data Mining Summary Notes

ChAPtEr 12 Evaluating Quants and Quant Strategies

Gathering Information Evaluating a Quantitative Trading Strategy Evaluating the Acumen of Quantitative Traders The Edge Evaluating Integrity How Quants Fit into a Portfolio Summary Note

175

176 180 184 186 193 195 195

197

197 199 204 206 207 210 213 213

215

216 218 221 223 227 229 231 233

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Contents

PArt fOUr

high-Speed and high-frequency trading ChAPtEr 13 An Introduction to high-Speed and high-frequency trading* Notes

ChAPtEr 14 high-Speed trading

237

241

243

Why Speed Matters Sources of Latency Summary Notes

244 252 262 263

ChAPtEr 15 high-frequency trading

Contractual Market Making Noncontractual Market Making Arbitrage Fast Alpha HFT Risk Management and Portfolio Construction Summary Note

ChAPtEr 16 Controversy regarding high-frequency trading

Does HFT Create Unfair Competition? Does HFT Lead to Front-Running or Market Manipulation? Does HFT Lead to Greater Volatility or Structural Instability? Does HFT Lack Social Value? Regulatory Considerations Summary Notes

265

265 269 271 273 274 277 277

279

280 283 289 296 297 299 300

ChAPtEr 17 Looking to the future of Quant trading

303

About the Author

307

Index

309

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Preface to the Second Edition

History is a relentless master. It has no present, only the past rushing into the future. To try to hold fast is to be swept aside. —John F. Kennedy

W

ithin the investment management business, a wildly misunderstood niche is booming, surely but in relative obscurity. This niche is populated by some of the brightest people ever to work in the field, and they are working to tackle some of the most interesting and challenging problems in modern finance. This niche is known by several names: quantitative trading, systematic trading, or black box trading. As in almost every field, technology is revolutionizing the way things are being done, and very rarely, also what is being done. And, as is true of revolutions generally (in particular, scientific ones), not everyone understands or likes what’s happening. I mentioned above that this is a technological revolution, which may have struck you as being strange, considering we’re talking about quant trading here. But the reality is that the difference between quant traders and discretionary ones is precisely a technological one. Make no mistake: Being good at investing almost always involves some math, whether it’s a fundamental analyst building a model of a company’s revenues, costs, and resulting profits or losses, or computing a price‐to‐earnings ratio. Graham and Dodd’s Security Analysis has a whole chapter regarding financial statement analysis, and that bible of fundamental value investing has more formulae in it than this book. Just as in any other application where doing things in a disciplined, repeatable, consistent way is useful—whether it’s in building cars or flying airplanes—investing can be systematized. It should be systematized. And quant traders have gone some distance down the road of systematizing it. Car building is still car building, whether it’s human hands turning ratchets or machines doing it. Flying a plane is not viewed differently when a human pilot does the work than when an autopilot does the work. In other words, the same work is being done, it’s just being done in a different way. This is a technological difference, at its heart.

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Preface to the Second edition

If I say, “I’d like to own cheap stocks,” I could, theoretically, hand‐ compute every company’s price‐to‐earnings ratio, manually search for the cheapest ones, and manually go to the marketplace to buy them. Or I could write a computer program that scans a database that has all of those price‐ to‐earnings ratios loaded into it, finds all the ones that I defined up‐front as being cheap, and then goes out and buys those stocks at the market using trading algorithms. The how part of the work is quite different in one case from the other. But the stocks I own at the end of it are identical, and for identical reasons. So, if what we’re talking about here is a completely rational evolution in how we’re doing a specific kind of work, and if we’re not being unreasonably technophobic, then why is it that reporters, politicians, the general public, and even many industry professionals really dislike quant trading? There are two reasons. One is that, in some cases, the dislike comes from people whose jobs are being replaced by technology. For example, it is very obvious that many of the most active opponents of high‐frequency trading are primarily fighting not out of some altruistic commitment to the purest embodiment of a capitalist marketplace (though that would be so terrifically ironic that I’d love if it were true), but because their livelihood is threatened by a superior way of doing things. This is understandable, and fair enough. But it’s not good for the marketplace if those voices win out, because ultimately they are advocating stagnancy. There’s a reason that the word Luddite has a negative connotation. A second reason, far more common in my experience, is that people don’t understand quant trading, and what we don’t understand, we tend to fear and dislike. This book is aimed at improving the understanding of quant trading of various types of participants in the investment management industry. Quants are themselves often guilty of exacerbating the problem by being unnecessarily cagey about even the broadest descriptions of their activities. This only breeds mistrust in the general community, and it turns out not to be necessary in the least. This book takes you on a tour through the black box, inside and out. It sheds light on the work that quants do, lifting the veil of mystery that surrounds quantitative trading, and allowing those interested in doing so to evaluate quants and their strategies. The first thing that should be made clear is that people, not machines, are responsible for most of the interesting aspects of quantitative trading. Quantitative trading can be defined as the systematic implementation of trading strategies that human beings create through rigorous research. In this context, systematic is defined as a disciplined, methodical, and automated approach. Despite this talk of automation and systematization, people conduct the research and decide what the strategies will be, people select

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Preface to the Second Edition

xv

the universe of securities for the system to trade, and people choose what data to procure and how to clean those data for use in a systematic context, among a great many other things. These people, the ones behind quant trading strategies, are commonly referred to as quants or quant traders. Quants employ the scientific method in their research. Though this research is aided by technology and involves mathematics and formulae, the research process is thoroughly dependent on human decision making. In fact, human decisions pervade nearly every aspect of the design, implementation, and monitoring of quant trading strategies. As I’ve indicated already, quant strategies and traditional discretionary investment strategies, which rely on human decision makers to manage portfolios day to day, are rather similar in what they do. The differences between a quant strategy and a discretionary strategy can be seen in how the strategy is created and in how it is implemented. By carefully researching their strategies, quants are able to assess their ideas in the same way that scientists test theories. Furthermore, by utilizing a computerized, systematic implementation, quants eliminate the arbitrariness that pervades so many discretionary trading strategies. In essence, decisions driven by emotion, indiscipline, passion, greed, and fear—what many consider the key pratfalls of playing the market—are eliminated from the quant’s investment process. They are replaced by an analytical and systematic approach that borrows from the lessons learned in so many other fields: If something needs to be done repeatedly and with a great deal of discipline, computers will virtually always outshine humans. We simply aren’t cut out for repetition in the way that computers are, and there’s nothing wrong with that. Computers, after all, aren’t cut out for creativity the way we are; without humans telling computers what to do, computers wouldn’t do much of anything. The differences in how a strategy is designed and implemented play a large part in the consistent, favorable risk/reward profile a well‐run quant strategy enjoys relative to most discretionary strategies. To clarify the scope of this book, it is important to note that I focus on alpha‐oriented strategies and largely ignore quantitative index traders or other implementations of beta strategies. Alpha strategies attempt to generate returns by skillfully timing the selection and/or sizing of various portfolio holdings; beta strategies mimic or slightly improve on the performance of an index, such as the S&P 500. Though quantitative index fund management is a large industry, it requires little explanation. Neither do I spend much time on the field of financial engineering, which typically plays a role in creating or managing new financial products such as collateralized debt obligations (CDOs). Nor do I address quantitative analysis, which typically supports discretionary investment decisions. Both of these are interesting

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Preface to the Second edition

subjects, but they are so different from quant trading as to be deserving of their own, separate discussions carried out by experts in those fields. This book is divided into four parts. Part One (Chapters 1 and 2) provides a general but useful background on quantitative trading. Part Two (Chapters 3 through 9) details the contents of the black box. Part Three (Chapters 10 through 12) is an introduction to highâ&#x20AC;?frequency trading, the infrastructure that supports such highâ&#x20AC;?speed trading, and some truths and myths regarding this controversial activity. Part Four (Chapters 13 through 16) provides an analysis of quant trading and techniques that may be useful in assessing quant traders and their strategies. Finally, Chapter 17 looks at the present and future of quant trading and its place in the investment world. It is my aspiration to explain quant trading in an intuitive manner. I describe what quants do and how they do it by drawing on the economic rationale for their strategies and the theoretical basis for their techniques. Equations are avoided, and the use of jargon is limited and explained, when required at all. My aim is to demonstrate that what many call a black box is in fact transparent, intuitively sensible, and readily understandable. I also explore the lessons that can be learned from quant trading about investing in general and how to evaluate quant trading strategies and their practitioners. As a result, Inside the Black Box may be useful for a variety of participants in and commentators on the capital markets. For portfolio managers, analysts, and traders, whether quantitative or discretionary, this book will help contextualize what quants do, how they do it, and why. For investors, the financial media, regulators, or anyone with a reasonable knowledge of capital markets in general, this book will engender a deeper understanding of this niche. Rishi K Narang

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Part

One the Quant Universe

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ChaPter

1

Why Does Quant trading Matter? Look into their minds, at what wise men do and don’t. —Marcus Aurelius, Meditations

J

ohn is a quant trader running a midsized hedge fund. He completed an undergraduate degree in mathematics and computer science at a top school in the early 1990s. John immediately started working on Wall Street trading desks, eager to capitalize on his quantitative background. After seven years on the Street in various quant‐oriented roles, John decided to start his own hedge fund. With partners handling business and operations, John was able to create a quant strategy that recently was trading over $1.5 billion per day in equity volume. More relevant to his investors, the strategy made money on 60 percent of days and 85 percent of months—a rather impressive accomplishment. Despite trading billions of dollars of stock every day, there is no shouting at John’s hedge fund, no orders being given over the phone, and no drama in the air; in fact, the only sign that there is any trading going on at all is the large flat‐screen television in John’s office that shows the strategy’s performance throughout the day and its trading volume. John can’t give you a fantastically interesting story about why his strategy is long this stock or short that one. While he is monitoring his universe of thousands of stocks for events that might require intervention, for the most part he lets the automated trading strategy do the hard work. What John monitors quite carefully, however, is the health of his strategy and the market environment’s impact on it. He is aggressive about conducting research on an ongoing basis to adjust his models for changes in the market that would impact him.

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The QuanT universe

Across from John sits Mark, a recently hired partner of the fund who is researching high‐frequency trading. Unlike the firm’s first strategy, which only makes money on 6 out of 10 days, the high‐frequency efforts Mark and John are working on target a much more ambitious task: looking for smaller opportunities that can make money every day. Mark’s first attempt at high‐frequency strategies already makes money nearly 95 percent of the time. In fact, their target for this high‐frequency business is even loftier: They want to replicate the success of those firms whose trading strategies make money every hour, maybe even every minute, of every day. Such high‐ frequency strategies can’t accommodate large investments, because the opportunities they find are small, fleeting. The technology required to support such an endeavor is also incredibly expensive, not just to build, but to maintain. Nonetheless, they are highly attractive for whatever capital they can accommodate. Within their high‐frequency trading business, John and Mark expect their strategy to generate returns of about 200 percent a year, possibly much more. There are many relatively small quant trading boutiques that go about their business quietly, as John and Mark’s firm does, but that have demonstrated top‐notch results over reasonably long periods. For example, Quantitative Investment Management of Charlottesville, Virginia, averaged over 20 percent per year for the 2002–2008 period—a track record that many discretionary managers would envy.1 On the opposite end of the spectrum from these small quant shops are the giants of quant investing, with which many investors are already quite familiar. Of the many impressive and successful quantitative firms in this category, the one widely regarded as the best is Renaissance Technologies. Renaissance, the most famous of all quant funds, is famed for its 35 percent average yearly returns (after exceptionally high fees), with extremely low risk, since 1990. In 2008, a year in which many hedge funds struggled mightily, Renaissance’s flagship Medallion Fund gained approximately 80 percent.2 I am personally familiar with the fund’s track record, and it’s actually gotten better as time has passed—despite the increased competition and potential for models to stop working. Not all quants are successful, however. It seems that once every decade or so, quant traders cause—or at least are perceived to cause—markets to move dramatically because of their failures. The most famous case by far is, of course, Long Term Capital Management (LTCM), which nearly (but for the intervention of Federal Reserve banking officials and a consortium of Wall Street banks) brought the financial world to its knees. Although the world markets survived, LTCM itself was not as lucky. The firm, which averaged 30 percent returns after fees for four years, lost nearly 100 percent of its capital in the debacle of August–October 1998 and left many investors

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Why Does Quant Trading Matter?

5

both skeptical and afraid of quant traders. Never mind that it is debatable whether this was a quant trading failure or a failure of human judgment in risk management, nor that it’s questionable whether LTCM was even a quant trading firm at all. It was staffed by PhDs and Nobel Prize–winning economists, and that was enough to cast it as a quant trading outfit, and to make all quants guilty by association. Not only have quants been widely panned because of LTCM, but they have also been blamed (probably unfairly) for the crash of 1987 and (quite fairly) for the eponymous quant liquidation of 2007, the latter having severely impacted many quant shops. Even some of the largest names in quant trading suffered through the quant liquidation of August 2007. For instance, Goldman Sachs’ largely quantitative Global Alpha Fund was down an estimated 40 percent in 2007 after posting a 6 percent loss in 2006.3 In less than a week during August 2007, many quant traders lost between 10 and 40 percent in a few days, though some of them rebounded strongly for the remainder of the month. In a recent best‐selling nonfiction book, a former Wall Street Journal reporter even attempted to blame quant trading for the massive financial crisis that came to a head in 2008. There were gaps in his logic large enough to drive an 18‐wheeler through, but the popular perception of quants has never been positive. And this is all before high‐frequency trading (HFT) came into the public consciousness in 2010, after the Flash Crash on May 10 of that year. Ever since then, various corners of the investment and trading world have tried very hard to assert that quants (this time, in the form of HFTs) are responsible for increased market volatility, instability in the capital markets, market manipulation, front‐running, and many other evils. We will look into HFT and the claims leveled against it in greater detail in Chapter 16, but any quick search of the Internet will confirm that quant trading and HFT have left the near‐total obscurity they enjoyed for decades and entered the mainstream’s thoughts on a regular basis. Leaving aside the spectacular successes and failures of quant trading, and all of the ills for which quant trading is blamed by some, there is no doubt that quants cast an enormous shadow on the capital markets virtually every trading day. Across U.S. equity markets, a significant, and rapidly growing, proportion of all trading is done through algorithmic execution, one footprint of quant strategies. (Algorithmic execution is the use of computer software to manage and work an investor’s buy and sell orders in electronic markets.) Although this automated execution technology is not the exclusive domain of quant strategies—any trade that needs to be done, whether by an index fund or a discretionary macro trader, can be worked using execution algorithms—certainly a substantial portion of all algorithmic trades are done by quants. Furthermore, quants were both the inventors and primary innovators

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6

The QuanT universe

of algorithmic trading engines. A mere five such quant traders account for about 1 billion shares of volume per day, in aggregate, in the United States alone. It is worth noting that not one of these is well known to the broader investing public, even now, after all the press surrounding high-frequency trading. The TABB Group, a research and advisory firm focused exclusively on the capital markets, estimates that, in 2008, approximately 58 percent of all buy‐side orders were algorithmically traded. TABB also estimates that this figure has grown some 37 percent per year, compounded, since 2005. More directly, the Aite Group published a study in early 2009 indicating that more than 60 percent of all U.S. equity transactions are attributable to short‐ term quant traders.4 These statistics hold true in non‐U.S. markets as well. Black‐box trading accounted for 45 percent of the volume on the European Xetra electronic order‐matching system in the first quarter of 2008, which is 36 percent more than it represented a year earlier.5 The large presence of quants is not limited to equities. In futures and foreign exchange markets, the domain of commodity trading advisors (CTAs), quants pervade the marketplace. Newedge Alternative Investment Solutions and Barclay Hedge used a combined database to estimate that almost 90 percent of the assets under management among all CTAs are managed by systematic trading firms as of August 2012. Although a great many of the largest and most established CTAs (and hedge funds generally) do not report their assets under management or performance statistics to any database, a substantial portion of these firms are actually quants also, and it is likely that the real figure is still over 75 percent. As of August 2012, Newedge estimates that the amount of quantitative futures money under management was $282.3 billion. It is clear that the magnitude of quant trading among hedge funds is substantial. Hedge funds are private investment pools that are accessible only to sophisticated, wealthy individual or institutional clients. They can pursue virtually any investment mandate one can dream up, and they are allowed to keep a portion of the profits they generate for their clients. But this is only one of several arenas in which quant trading is widespread. Proprietary trading desks at the various banks, boutique proprietary trading firms, and various multistrategy hedge fund managers who utilize quantitative trading for a portion of their overall business each contribute to a much larger estimate of the size of the quant trading universe. With such size and extremes of success and failure, it is not surprising that quants take their share of headlines in the financial press. And though most press coverage of quants seems to be markedly negative, this is not always the case. In fact, not only have many quant funds been praised for their steady returns (a hallmark of their disciplined implementation process), but some experts have even argued that the existence of successful

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Why Does Quant Trading Matter?

7

quant strategies improves the marketplace for all investors, regardless of their style. For instance, Reto Francioni (chief executive of Deutsche Boerse AG, which runs the Frankfurt Stock Exchange) said in a speech that algorithmic trading “benefits all market participants through positive effects on liquidity.” Francioni went on to reference a recent academic study showing “a positive causal relationship between algo trading and liquidity.”6 Indeed, this is almost guaranteed to be true. Quant traders, using execution algorithms (hence, algo trading), typically slice their orders into many small pieces to improve both the cost and efficiency of the execution process. As mentioned before, although originally developed by quant funds, these algorithms have been adopted by the broader investment community. By placing many small orders, other investors who might have different views or needs can also get their own executions improved. Quants typically make markets more efficient for other participants by providing liquidity when other traders’ needs cause a temporary imbalance in the supply and demand for a security. These imbalances are known as inefficiencies, after the economic concept of efficient markets. True inefficiencies (such as an index’s price being different from the weighted basket of the constituents of the same index) represent rare, fleeting opportunities for riskless profit. But riskless profit, or arbitrage, is not the only—or even primary—way in which quants improve efficiency. The main inefficiencies quants eliminate (and, thereby, profit from) are not absolute and unassailable, but rather are probabilistic and require risk taking. A classic example of this is a strategy called statistical arbitrage, and a classic statistical arbitrage example is a pairs trade. Imagine two stocks with similar market capitalizations from the same industry and with similar business models and financial status. For whatever reason, Company A is included in a major market index, an index that many large index funds are tracking. Meanwhile, Company B is not included in any major index. It is likely that Company A’s stock will subsequently outperform shares of Company B simply due to a greater demand for the shares of Company A from index funds, which are compelled to buy this new constituent in order to track the index. This outperformance will in turn cause a higher P/E multiple on Company A than on Company B, which is a subtle kind of inefficiency. After all, nothing in the fundamentals has changed—only the nature of supply and demand for the common shares. Statistical arbitrageurs may step in to sell shares of Company A to those who wish to buy, and buy shares of Company B from those looking to sell, thereby preventing the divergence between these two fundamentally similar companies from getting out of hand and improving efficiency in market pricing. Let us not be naïve: They improve efficiency not out of altruism, but because these strategies are set up to profit if indeed a convergence occurs between Companies A and B.

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The QuanT universe

This is not to say that quants are the only players who attempt to profit by removing market inefficiencies. Indeed, it is likely that any alpha‐oriented trader is seeking similar sorts of dislocations as sources of profit. And, of course, there are times, such as August 2007, when quants actually cause the markets to be temporarily less efficient. Nonetheless, especially in smaller, less liquid, and more neglected stocks, statistical arbitrage players are often major providers of market liquidity and help establish efficient price discovery for all market participants. So, what can we learn from a quant’s approach to markets? The three answers that follow represent important lessons that quants can teach us—lessons that can be applied by any investment manager.

the Benefit Of DeeP thOUght According to James Simons, the founder of the legendary Renaissance Technologies, one of the greatest advantages quants bring to the investment process is their systematic approach to problem solving. As Dr. Simons puts it, “The advantage scientists bring into the game is less their mathematical or computational skills than their ability to think scientifically.”7 The first reason it is useful to study quants is that they are forced to think deeply about many aspects of their strategy that are taken for granted by nonquant investors. Why does this happen? Computers are obviously powerful tools, but without absolutely precise instruction, they can achieve nothing. So, to make a computer implement a black‐box trading strategy requires an enormous amount of effort on the part of the developer. You can’t tell a computer to “find cheap stocks.” You have to specify what find means, what cheap means, and what stocks are. For example, finding might involve searching a database with information about stocks and then ranking the stocks within a market sector (based on some classification of stocks into sectors). Cheap might mean P/E ratios, though one must specify both the metric of cheapness and what level will be considered cheap. As such, the quant can build his system so that cheapness is indicated by a 10 P/E or by those P/Es that rank in the bottom decile of those in their sector. And stocks, the universe of the model, might be all U.S. stocks, all global stocks, all large cap stocks in Europe, or whatever other group the quant wants to trade. All this defining leads to a lot of deep thought about exactly what one’s strategy is, how to implement it, and so on. In the preceding example, the quant doesn’t have to choose to rank stocks within their sectors. Instead, stocks can be compared to their industry peers, to the market overall, or to any other reasonable group. But the point is that the quant is encouraged to

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9

Why Does Quant Trading Matter?

be intentional about these decisions by virtue of the fact that the computer will not fill in any of these blanks on its own. The benefit of this should be self‐evident. Deep thought about a strategy is usually a good thing. Even better, this kind of detailed and rigorous working out of how to divide and conquer the problem of conceptualizing, defining, and implementing an investment strategy is useful to quants and discretionary traders alike. These benefits largely accrue from thoroughness, which is generally held to be a key ingredient to investment or trading success. By contrast, many (though certainly not all) discretionary traders, because they are not forced to be so precise in the specification of their strategy and its implementation, seem to take a great many decisions in an ad hoc manner. I have been in countless meetings with discretionary traders who, when I asked them how they decided on the sizes of their positions, responded with variations on the theme of “Whatever seemed reasonable.” This is by no means a damnation of discretionary investment styles. I merely point out that precision and deep thought about many details, in addition to the bigger‐picture aspects of a strategy, can be a good thing, and this lesson can be learned from quants.

the MeasUreMent anD MisMeasUreMent Of risk As mentioned earlier in this chapter, the history of LTCM is a lesson in the dangers of mismeasuring risk. Quants are naturally predisposed toward conducting all sorts of measurements, including of risk exposure. This activity itself has potential benefits and downsides. On the plus side, there is a certain intentionality of risk taking that a well‐conceived quant strategy encourages. Rather than accepting accidental risks, the disciplined quant attempts to isolate exactly what his edge is and focus his risk taking on those areas that isolate this edge. To root out these risks, the quant must first have an idea of what these risks are and how to measure them. For example, most quant equity traders, recognizing that they do not have sufficient capabilities in forecasting the direction of the market itself, measure their exposure to the market (using their net dollar or beta exposure, commonly) and actively seek to limit this exposure to a trivially small level by balancing their long portfolios against their short portfolios. On the other hand, there are very valid concerns about false precision, measurement error, and incorrect sets of assumptions that can plague attempts to measure risk and manage it quantitatively. All the blowups we have mentioned, and most of those we haven’t, stem in one way or another from this overreliance on flawed risk measurement techniques. In the case of LTCM, for example, historical data showed that certain scenarios were likely, others unlikely, and still others had simply never

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10

The QuanT universe

occurred. At that time, most market participants did not expect that a country of Russia’s importance, with a substantial supply of nuclear weapons and materials, would go bankrupt. Nothing like this had ever happened before. Nevertheless, Russia indeed defaulted on its debt in the summer of 1998, sending the world’s markets into a frenzy and rendering useless any measurement of risk. The naïve overreliance on quantitative measures of risk, in this case, led to the near‐collapse of the financial markets in the autumn of 1998. But for a rescue orchestrated by the U.S. government and agreed on by most of the powerhouse banks on Wall Street, we would have seen a very different path unfold for the capital markets and all aspects of financial life. Indeed, the credit debacle that began to overwhelm markets in 2007 and 2008, too, was likely avoidable. Banks relied on credit risk models that simply were unable to capture the risks correctly. In many cases, they seem to have done so knowingly, because it enabled them to pursue outsized short‐term profits (and, of course, bonuses for themselves). It should be said that most of these mismeasurements could have been avoided, or at least the resulting problems mitigated, by the application of better judgment on the part of the practitioners who relied on them. Just as one cannot justifiably blame weather‐forecasting models for the way that New Orleans was impacted by Hurricane Katrina in 2005, it would not make sense to blame quantitative risk models for the failures of those who created and use them. Traders can benefit from engaging in the exercise of understanding and measuring risk, so long as they are not seduced into taking ill‐advised actions as a result.

DisCiPlineD iMPleMentatiOn Perhaps the most obvious lesson we can learn from quants comes from the discipline inherent to their approach. Upon designing and rigorously testing a strategy that makes economic sense and seems to work, a properly run quant shop simply tends to let the models run without unnecessary, arbitrary interference. In many areas of life, from sports to science, the human ability to extrapolate, infer, assume, create, and learn from the past is beneficial in the planning stages of an activity. But execution of the resulting plan is also critical, and it is here that humans frequently are found to be lacking. A significant driver of failure is a lack of discipline. Many successful traders subscribe to the old trading adage: Cut losers and ride winners. However, discretionary investors often find it very difficult to realize losses, whereas they are quick to realize gains. This is a well‐documented behavioral bias known as the disposition effect.8 Computers, however, are not subject to this bias. As a result, a trader who subscribes to the aforementioned

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Why Does Quant Trading Matter?

11

adage can easily program his trading system to behave in accordance with it every time. This is not because the systematic trader is somehow a better person than the discretionary trader, but rather because the systematic trader is able to make this rational decision at a time when there is no pressure, thereby obviating the need to exercise discipline at a time when most people would find it extraordinarily challenging. Discretionary investors can learn something about discipline from those who make it their business.

sUMMary Quant traders are a diverse and large portion of the global investment universe. They are found in both large and small trading shops and traffic in multiple asset classes and geographical markets. As is obvious from the magnitude of success and failure that is possible in quant trading, this niche can also teach a great deal to any curious investor. Most traders would be well served to work with the same kind of thoroughness and rigor that is required to properly specify and implement a quant trading strategy. Just as useful is the quant’s proclivity to measure risk and exposure to various market dynamics, though this activity must be undergone with great care to avoid its flaws. Finally, the discipline and consistency of implementation that exemplifies quant trading is something from which all decision makers can learn a great deal.

nOtes 1. M. Corey Goldman, “Hot Models Rev Up Returns,” HFMWeek.com, April 17, 2007; Jenny Strasburg and Katherine Burton, “Goldman Sachs, AQR Hedge Funds Fell 6% in November (Update3),” Bloomberg.com, December 7, 2007. 2. Gregory Zuckerman, Jenny Strasburg, and Peter Lattman, “Renaissance Waives Fees on Fund That Gave Up 12%,” Wall Street Journal Online, January 5, 2009. 3. Lisa Kassenaar and Christine Harper, “Goldman Sachs Paydays Suffer on Lost Leverage with Fed Scrutiny,” Bloomberg.com, October 21, 2008. 4. Sang Lee, “New World Order: The High Frequency Trading Community and Its Impact on Market Structure,” The Aite Group, February 2009. 5. Peter Starck, “Black Box Trading Has Huge Potential—D. Boerse,” Reuters.com, June 13, 2008. 6. Terry Hendershott, Charles M. Jones, and Albert J. Menkveld, “Does Algorithmic Trading Improve Liquidity?” WFA Paper, April 26, 2008. 7. www.turtletrader.com/trader‐simons.html. 8. Hersh Shefrin and Meir Statman, “The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence,” Journal of Finance 40, no. 3 (July 1985).

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ANDREW SMITHERS Foreword by Martin Wolf, Chief Economics Commentator, Financial Times

THE ROAD TO HOW AND WHY ECONOMIC POLICY MUST CHANGE click to learn more about this book on wiley.com


Contents Foreword

ix

Chapter 1 Introduction

1

Chapter 2 Why the Recovery Has Been So Weak

3

Chapter 3 Alternative Explanations for Todayâ&#x20AC;&#x2122;s Low Business Investment and High Profit Margins

47

Chapter 4 Forecasting Errors in the UK and the US

61

Chapter 5 Cyclical or Structural: The Key Issue for Policy

69

Chapter 6 The Particular Problem of Finance and Banking

81

Chapter 7 Japan Has a Similar Problem with a Different Cause

107

Chapter 8 The End of the Post-War Era

125

Chapter 9 Misinformation as a Barrier to Sound Policy Decisions

149

Chapter 10 Avoiding Future Financial Crises

169

Chapter 11 The Current High Level of Risk

179

Chapter 12 Inflation

195

Chapter 13 Prospects Not Forecasts

219

Chapter 14 Tackling the Bonus Culture

229

Chapter 15 The Need for Change in Economic Theory and the Resistance to It

237

v

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vi

contents

Chapter 16 Summary and Conclusions

255

Appendix 1 Mean Reversion of US Profit Margins

259

Appendix 2 Goodsâ&#x20AC;&#x2122; Output Requires Much More Capital Than Service Output

261

Bibliography

263

Acknowledgements

269

Index

271

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1 Introduction

The world economy is badly managed and thus doing badly. The financial crisis caused the most severe recession since the depression of the 1930s. The fall in output has been arrested but the recovery has been disappointing. If neither the crisis nor the weak rebound were inevitable, we must be suffering from policy mistakes. Either economic theory is sound but being badly applied or it contains serious weaknesses. In this book I will seek to explain what has gone wrong and the steps needed to put the world economy back on track for a sustained recovery. The errors of policy have their sources both from failures to understand and apply the parts of economic theory which are sound and from failures in the generally accepted theory, which policymakers have sought to follow. The economic policies of the eurozone fall into the first category. For the zone as a whole, short-term fiscal policy should be aimed at expanding rather than contracting deficits, and my view is probably shared by a majority of economists. But there are two areas where, I think, theory has failed. The first lies in misunderstanding the causes of the crisis and thus the policies needed to prevent its repetition. The second is the failure to recognise, and thus be able to remove, the obstacles that currently prevent sustained recovery in Japan, the UK and the US. 1

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2

the road to recove ry

With regard to the crisis, there are many issues over which the views of economists diverge, and many of the points I will be making are shared by others. At the moment, however, I seem to be more or less alone in my identification of the problems currently impeding recovery, a situation which I hope this book will change. If I am correct, the vast bulk of the current debate on economic policy is misdirected and new policies are needed to produce a more satisfactory recovery in terms of both its speed and its sustainability. I aim to convince the reader that the financial crisis, the great recession which it produced and the failure to generate a strong recovery are all the results of policy errors in the management of the economy, and I will rely heavily on data in my task of persuasion. I will use many charts because these are often the easiest way to communicate the data’s messages. They will also provide pictures as I am mindful of Alice’s comment, when looking at her elder sister’s book and about to nod off to sleep to dream of Wonderland. “What is the use of a book,” she remarks, “without pictures or conversations?”1 Even in the form of quotations, I have been able to include only a limited amount of conversation, but to compensate for this and console readers for its absence, they will find plenty of pictures.

1

From Chapter 1 of Alice’s Adventures in Wonderland by Lewis Carroll.

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The chapters from this sampler are extracted from the following titles:

Emerging Markets in an Upside Down World 9781118879672, March 2014

Global Leaders in Islamic Finance 9781118465240, November 2013

Better Banking 9781118651308, December 2013

Rebuilding Trust in Banks 9781118550380, November 2013

Correlation Risk Modeling and Management 9781118796900, November 2013

The Bank Credit Analysis Handbook 9780470821572, April 2013

Inside the Black Box 9781118362419, March 2013

The Road to Recovery

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9781118515662, September 2013

The Asian Banker Summit 2014 eSampler  

The Wiley e-book Sampler includes selected material from Wiley’s leading titles on Banking & Finance, including the book’s full Table of Con...

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