RiskMinds Americas - June 2015

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“BASEL IV” & THE MOVE TO STANDARDISED APPROACHES

MAKE STRESS TESTING AN INTEGRATED PART OF YOUR BUSINESS

TRUST IN THE NEW WORLD: THE EVOLVING WORLD OF THE CRO

June 15-19, 2015, JW Marriot Marquis1Miami - www.riskmindsamericas.com Join the conversation #RMUS15

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WELCOME Welcome to the first issue of the RiskMinds Americas magazine!

It is a pleasure to welcome you to Miami for the 6th annual RiskMinds Americas conference.

Many things have changed in the six years we have been running the event – new regulations have been created, CCAR has become an increasing draw on the time and resources of banks’ risk management departments, and banks are facing new challenges. Furthermore, the evolving geopolitical environment, technological changes and the emergence of new competitors means there are many new risks impacting banks and other financial firms.

However, while new technologies are creating new risks, such as cyber attacks and threats to information security, they are also bringing about new opportunities. Big data offers the prospect of improving the customer experience but also making risk management more effective. Similarly, while the CCAR process can raise challenges for banks, it can also be used to improve strategic decision making within banks and ensure it is more informed by risk management considerations.

Among the many people I have spoken to while preparing for this event over the last 9 months, one definite consensus has emerged: we are living in interesting times!

If one thing seems certain, it is that, over the coming years, we can expect many more changes, affecting both risk managers and the financial industry. However, one thing that will not change will be the need for proactive, empowered and informed risk management.

This magazine includes articles examining some of these key issues facing risk managers today. We hope you find it a useful supplement to your time at the event.

If you have any feedback or any ideas for next year’s event do let me know.

I look forward to having the opportunity to discuss the future of the industry with you over the next few days.

MARIE HOUGHTON, CONFERENCE DIRECTOR

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CONTENTS 6

THE CIRCLE OF (MODELING) LIFE

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WHEN WILL THE MIDDLE EAST SETTLE DOWN?

TRUST IN THE NEW WORLD: THE EVOLVING ROLE OF THE CRO

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OPPORTUNITIES FOR BIG DATA IN CREDIT MARKETS

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RISKMINDS AMERICAS SOCIAL EVENTS

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MAKE STRESS TESTING AN INTEGRATED PART OF YOUR BUSINESS CREDIT MARKET BUBBLE BUILDING SPECIAL THANKS AND CREDITS RISKMINDS AMERICAS 2015 CONFERENCE DIRECTOR Marie Houghton

SPONSORS Bloomberg, Bosch, KPMG, LifeLock, Oracle Financial Services, QRM, SAS, SEBA International.

RISKMINDS AMERICAS MAGAZINE EDITOR Susan Buckley

SPONSORSHIP MANAGER Rustum Bharuca

CONTRIBUTORS Ed Altman, Tomer Barel, Terry Benzschawel, Philip Lofts, Thomas Kimner, Marvin Zonis

ARTISTIC DIRECTOR Lock On Productions PRINT TEAM Trio Offset

If you are interested in submitting content for a future RiskMinds Americas magazine or require any sponsorship information for the 2016 event, please email Rustum Bharucha rbharucha@icbi.co.uk

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THE CIRCLE OF (MODELLING) LIFE In December 2014, the Basel Committee issued two consultative documents, BCBS 306 and BCBS 307, proposing changes in how banks calculate capital. The proposals include setting capital floors using a new simplified Standardised Approach and thereby reduce reliance on models.

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et’s remind ourselves of the history of models as used by banks and their supervisors to determine appropriate capital underpinning. Ratios of capital to deposits were used by U.S. regulators to monitor banks from the early 1900s, and subsequently capital to asset ratios were used from the late 1930s.

After WWII U.S. regulators introduced ratios of capital to Total Assets, and Capital to “Risk Assets”, from which measures the Leverage Ratio and the Cooke Ratio used in the Basel Accord of 1988 (Basel I) was derived.

Shortcomings of risk-insensitive measures such as the Leverage Ratio became apparent over time, as they motivated banks to assume the riskiest possible exposures per unit of balance sheet consumption. Basel I attempted to create a somewhat more risk-sensitive measure. It did

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not take long until problems began to surface with Basel I, resulting from banks’ natural business incentives to optimize their balance sheets and to maximize their potential riskadjusted returns, based on the Basel I capital requirements. The crude risk-stratification motivated banks to increase their exposure to assets for which Basel I under-estimated the capital requirement.

Consequently the Basel Committee created a new regime, Basel II, which gave banks varying degrees of freedom to adopt internal measures of EAD, PD and LGD. By allowing banks to use their internal risk parameters within the prescribed Pillar I formulations of capital requirement, the Basel Committee’s hope was that this would create a more-risksensitive capital framework. However, there is now a concern that RWA

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variability between banks is too high.

Therefore, even though there is no evidence that this regime failed for the capital underpinning of credit risk, to reduce the variability, the Basel Committee is considering the reintroduction of standardized capital models to determine minimumcapital floors. In re-introducing simpler models such as standardized approaches and the Leverage Ratio, regulators are coming full-circle, back to the old approaches which have already been seen to have failed.

The proposals will have profound effects on the relative distribution of capital across jurisdictions, banks and types of loan. Depending on the ultimate calibration of the floor, they are likely also to boost overall capital levels substantially and consequently have implications for the credit market, more dramatically

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in those country where the access to credit relies mainly on banks (and less on debt/capital markets, or alternative lenders).

By calculating the impact on the capital that banks will have to hold against loans using the proposed new Standardised Approach rules, and assuming that banks will adjust the pricing to maintain today’s return on equity, it is possible to estimate the increase in loan spreads, which reflect bank funding costs. Banks finance a given loan using a combination of deposit and equity funding. The latter is considerably more expensive to a bank, dollar for dollar. The volume amount of equity funding that a bank requires for a loan is determined by the regulatory capital a bank is required to hold. Changes in regulatory capital requirements, therefore, alter bank funding costs and

feed through into the interest rates banks demand from borrowers.

Our analyses reveal dramatic and in some cases, we suspect, unintended effects of the proposed regulations.

Capital for corporate loans would be substantially boosted by the rule changes whereas capital for residential mortgages would fall for SA banks (while increasing for IRBA banks). IRBA banks would be required to hold substantially greater capital whereas capital for SA banks would in many cases fall.

Whether, or not, these are desirable outcomes is debatable.

In re-introducing simpler models such as standardized approaches it may seem that regulators are coming full circle. Hopefully, old lessons will not need to be re-learned.

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PHILIP LOFTS Group Chief Risk Officer, UBS Philip Lofts will presenting the RiskMinds CRO Keynote Address on Effective Risk Management In A FastChanging World at 8.40am on Tuesday June 16. He will also be joining the CRO ThinkTank I discussing Profitability, Innovation & Regulation at 10.35am on the same day.

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WHEN WILL THE MIDDLE EAST SETTLE DOWN? Sigmund Freud suggested that psychoanalysis could convert one’s neurotic misery to everyday unhappiness. Leaping to geopolitics from psychoanalysis leads to the question of when the Middle East will be converted from massive chaos to everyday turmoil.

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hinking about that reminds me of one of my favorite New Yorker cartoons. An important executive is sitting at his mammoth desk and barking into the phone, “How about never? Is never soon enough?”

Is “never” when the Middle East settles down? In the case of the Middle East, never is probably too long a time. But don’t expect any return to conventional states and normal inter-state relations for the next two decades at the earliest. More likely, it will be closer to fifty years.

Just look at the issues in the three most troubled countries:

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Is “never” when the Middle East settles down?

In the case of the Middle East, never is probably too long a time.

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IRAQ

The persisting ills of Iraq are all too clear: • The destruction of the country’s political, physical, and economic infrastructure by the U.S. military onslaught.

• A government strongly influenced

by Iran that has monopolized its benefits for the country’s majority Shiite population.

• Kurdish leaders who seized the opportunity of the weakness in Baghdad to achieve a strong measure of autonomy.

• Senior Sunni officers of Saddam’s military, unwisely dismantled by U.S. ruler Paul Bremer, disgruntled at their loss of status.

• Sunni tribal leaders resentful of Shiite dominance.

• Jihadi Sunnis, intent on restoring greatness by a return to the Islam of the Prophet in the 7th century.

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What has emerged is the Islamic State that controls a substantial portion of Iraq, the semi-autonomous Kurdistan Regional Government that controls another major portion and a rump central government facing a country beset by violence.

How these forces work themselves out, if they ever do, and whether the geography of Iraq can remain intact are questions that will produce answers, but only over decades.

SYRIA

The heart of the region’s chaos is here.

All the elements that have brought a fire storm to the region can be found in Syria: Sunnis vs. Shia. Salafists vs. secularists. Jihadists vs. mainstream Muslims. A ruthless ruling family willing to kill its own citizens in staggering numbers in order to hold onto power. Radical Islamic groups that kill other competing extremists.

Foreign fighters and military advisers.

Interference from regional and extraregional states.

RISKMINDSAMERIC A S President Assad has faced recent setbacks, especially the loss of the northern provincial capital, Idlib.

Setback or not, it does not appear that the Assads will disappear anytime soon. But when they inevitably do, the country will further disintegrate into regions controlled by competing Islamic factions and Assad loyalists.

The governing infrastructure will face further collapse and the prospects in the next two decades for a restoration of a meaningful, non-violent Syria appear to be zero.

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President Hadi plus his new allies, Saudi Arabia and the UAE. Iran has been a supporter of the Houthis but its precise role, particularly the extent to which it has supplied them with weapons, is uncertain.

What is certain is that the violence will not subside any time soon, let alone the building of a central government that controls the entire country.

The Middle East is in the midst of the ‘perfect storm.’

YEMEN

Never a fully unified state under the control of the capital, Yemen has further fractured. A large contingent of radical Islamic extremists – Al Qaeda in the Arabian Peninsula control part of its territory and now Mukalla, a port city allowing them to receive supplies. The Islamic State, not to be outdone, has sent in its own troops.

Whatever central power had existed has collapsed with the war between the Houthis, local Zaydis who are a sect of Shiites joined by the former president of Yemen, Ali Abdullah Saleh, on the one hand, and, on the other hand, Saleh’s successor,

resist the pull of the region’s ills: the historical failures of its authoritarian rulers to build national identities or meaningful economies; the sectarian conflicts within Islam driven by Iran and Saudi Arabia; the rise of radical Islamists; the over reaching of Iran’s leaders; the invasion of Afghanistan and Iraq by the U.S. and its subsequent effort to rebuild both countries in its own image as market driven democracies; the reliance on oil revenues and the rise of rentier economies; The ‘perfect storm’ will eventually subside – as do all storms. The Middle East will settle down. But it will not for decades.

The ‘Perfect Storm’ the Middle East William Peace Thackeray first used the phrase in his novel, Vanity Fair: “I have heard a brother of the storytelling trade at Naples preaching and. . . work himself up into such a rage and passion. . . that the audience could not resist it; and they and the poet together would burst out into a roar of oaths and execrations against the fictitious monster of the tale that the hat went round. . .in the midst of a perfect storm of sympathy.”

The Middle East is in the midst of the ‘perfect storm.’ No group seems able to

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MARVIN ZONIS Professor Emeritus, Booth School Of Business, UNIVERSITY OF CHICAGO Marvin Zonis will be speaking about Geopolitical Risk, presenting on The World in Disarray: Terrorism, Wars, Sanctions, Competitive Devaluations & Deflation - Expect A Slow Growth Future at 3.00pm on Tuesday June 16.

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SPONSORED ARTICLE

MAKE STRESS TESTING

AN INTEGRATED PART OF YOUR BUSINESS

Government-mandated stress tests are designed to help regulators understand how well banks – and the financial system overall – can withstand adverse economic scenarios. But what about insights for banks? Have banks realized any business value from their investments in stress testing?

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he answer to date, in most cases, has been “not really.”

Why? The reason is twofold.

First, the focus of many current stress tests comes from a regulatory perspective that is concerned more with systemic risk, not specific risks facing individual banks. For example, systemic stress tests can’t help bank managers and executives discern when they are overstepping risk limits when issuing mortgages to new customers. Second, banks do not run their businesses based on stress scenarios – they run their businesses on expectations of a range of more likely market events. Sometimes these market events or scenarios are quite conservative, but they are rarely as adverse as regulatory stress events.

So it’s no surprise that in a 2014 SAS survey of 100 senior risk and

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41 percent of banks indicated that stress testing is not yet influencing business decisions. finance executives, 41 percent of banks indicated that stress testing is not yet influencing business decisions. While those results may not seem surprising, SAS research also found that banks want to make stress testing an integrated part of the business (as opposed to an annual regulatory exercise) to drive better decision making. The fact is, operational, legal, compliance and other risks for banks have increased over the years. And, business-specific insights are exactly what’s needed to successfully walk the tightrope between opportunity and risk.

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THE QUESTION IS, WHAT’S THE BEST WAY TO DO THIS?

The answer is, by running automated, business-specific scenario analyses that combine traditional VaR scenarios with stress tests (complete with agreed-upon risk limits) based on probabilities that business managers deem realistic.

With the right models, analytics and infrastructure in place, banks can efficiently stress test all risk components on both an aggregated and individual basis, including market, credit, liquidity, operational, business, strategic and reputational risk. Then, integrate this data into a dashboard that can support day-today capital adequacy considerations.

KEYS TO SUCCESS

Are you ready to implement business-specific scenario analysis and stress testing? SAS recommends applying the following best practices:

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• Know your organization’s risk limits, including the maximum amount of risk the bank is willing to accept across key areas of the business. These risk limits must have teeth – and be upheld all the way to the board level.

• Understand the business rationale for stress tests and how and where to get the data. This requires having a committee that knows what other business-specific stress tests are wanted and needed, where relevant data is located, and more.

• Acquire data from many systems needed to build and govern models to create a single, trusted version that’s available across the enterprise.

• Build a more automated scenario generation and stress test environment. It should streamline compliance activities and support business-specific stress testing for better business management. For example, you should be able to stress test capital levels when changes are performed to the IRB system (e.g., when a credit risk model is implemented) to assess the impact of regulatory changes on the business.

• Bring results together in a dashboard for a holistic, enterprise view of risk.

THOMAS KIMNER Head Of Americas Risk Practice, SAS INSTITUTE Thomas Kimner will be presenting Turbocharge Stress Testing With A HighPerformance Model Implementation Platform at 12.10pm on Wednesday June 17. SAS will also be holding their lunchtime Interactive Discussion On Stress Testing Challenges & Best Practices, led by Prashant Pai, Director of Global Risk & Cyber Analytics, at 12.55pm on Tuesday June 16.

Read more about SAS Risk Management solutions at sas.com/risk.

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CREDIT MARKET BUBBLE BUILDING? A FORMING CREDIT BUBBLE COULD BURST BY 2017/2018 Bubble theories and concerns are becoming quite common these days for several asset classes, prompting discussions and warnings, including those from federal regulators. This leads to some key questions: Are we in the midst of an inflating credit bubble and, if so, when is it likely to burst?

Contrarily, are we experiencing an extended period of opportunistic debt financing? typically reaches at least 10% for one or two years. With the market’s size now significantly larger than during prior default peaks, the amount of defaulted high-yield bonds likely to occur in stressed cycles will be between $150 billion to $200 billion in each 10% default year. We observe the aboveaverage default rates lasted three years in the 1989-1991 period, four years, or so, in the 2000-2003 period and just two years in the 2008-2009 cycle, averaging about three years.

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he evidence we have compiled leads us to conclude that, indeed, a bubble is building; but it is not likely to explode dramatically, with a significant increase in corporate defaults, until at least late 2016 or, more likely, in 2017-2018. Fear, however, of a potential crisis in banking credit and equity markets may contribute to periods of negative price movements in these and other asset classes before the bubble actually bursts. This is consistent with our expectation of a below-average default rate for the next 12-24 months. However, we are becoming quite concerned about the period thereafter, when the current benign

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credit cycle will reach six or seven years in length, the latter tying a record in modern high-yield bond history.

BENIGN AND DISTRESSED CYCLES: HOW LONG DO THEY LAST? Recent benign credit-cycles, of wellbelow-average default rate periods have lasted four years (2004-2007) and seven years (1992-1998), with the current cycle now in its sixth year. The average benign credit cycle since 1971 averaged about six years.

Once a benign cycle ends, the subsequent spike in default rates

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It should be noted that the three recent spikes in default rates to levels of 10% or more were accompanied by economic recessions. Forecasting the timing of economic recessions is challenging, at best, and to estimate the confluence of a stressed credit cycle with recession is a “perfect storm,” but one that has occurred a number of times in the recent

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Fear, however, of a potential crisis in banking credit and equity markets may contribute to periods of negative price movements in these and other asset classes before the bubble actually bursts

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past. And it is likely to occur again – predicting the timing remains the difficult issue.

NEW ISSUANCE AND CREDIT QUALITY

The corporate HY and Investment Grade (IG) sectors have been refinancing and increasing their debt financing continuously since the current benign cycle started in 2010.

Indeed, new HY issuance topped $200 billion for the first time in 2010, reached a peak of $280 billion in 2012 and almost matched that in 2013 and 2014. The growth of new HY issuance in Europe has been even more impressive of late, reaching €92 billion in 2013 and €119 B in 2014.

In a nutshell, market acceptance of newly issued high-yield junk bonds has been remarkable, with record amounts issued at relatively low interest rates. This reinforces that a seemingly insatiable appetite exists for higher yields in this low-interest rate environment. New issuance of leveraged loans has also grown dramatically of late, from just $77 billion in 2009 to a record $607 billion in 2013. The amount of new issuance was $530 billion in 2014.

In terms of the bond rating ascribed to new issues, recent trends also indicated deterioration in credit quality, notably the proportion of newly issued bonds rated “CCC” compared to total HY new issuance, from 2005 through the third quarter of 2014.

two quarters’ results are not enough to proclaim a serious escalation in risk tolerance, but it is still an ominous indication of significant defaults down the road. Our mortality rate statistics show that the average three-year cumulative mortality (default rate for “CCC” new issuance) is about 34% and, by the fifth year, it reaches 47.4%. We need to keep our eye on the results over the next few years of this low-rating cohort, as well as to keep in mind the even more plentiful and still risky “B” rating category.

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In a nutshell, market acceptance of newly issued highyield junk bonds has been remarkable, with record amounts issued at relatively low interest rates.

LBO STATISTICS AND TRENDS

With respect to highly leveraged LBO financing, we can observe that the purchase-price multiple increased to an incredible 10.9 times in the third-quarter of 2014 and 11.9 times in the first quarter 2015. Again, this is strongly reminiscent of, in fact exceeding, the frothy 2007 multiple.

Average total debt/EBITDA has risen to dangerous levels in the United States, the highest since 2007.

Skeptics might argue that these high-risk levels are acceptable in this low interest rate environment.

However, this can change quickly, especially if the economy falters and the Federal Reserve takes its foot off the accelerator.

Whether this tolerant, highly liquid market sentiment persists and the Fed continues its strong growth posture are topics continuing to warrant prominent analysis.

EDWARD ALTMAN Professor Of Finance & Director Of Fixed Income & Credit Market Research, NYU STERN SCHOOL OF BUSINESS Ed Altman will be presenting New Research In Credit Markets: Exploring The Latest Outlook For Loan Markets, Syndicated Lending, Leveraged Loans & The Impact Of Rates On Credit Markets at 8:55am on Thursday June 18. Ed will be available to speak with at the Meet the Speaker Lunchtables on the same day at 12:10pm.

The second quarter’s CCC proportion jumped to 25.9%, second only to the 2007 record 37.4%, when just about any credit quality company could find new bond financing. Issuance of CCC rated bonds fell in the fourth quarter of 2014 and first quarter of 2015. Still, an average of 22.1% for the first two quarters of 2014 was quite high. Now,

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TRUST IN THE NEW WORLD: THE EVOLVING ROLE OF THE CRO

There have been some dramatic changes in our industry in the last few years. Businesses and consumers are faced with new threats. According to LexisNexis, fraud costs U.S. retailers approximately $32 billion in 2014, up from $23 billion just one year earlier. Additionally, between 500 million and one billion identities were stolen globally last year due to data breaches.

The way we view Chief Risk Officers is evolving with this changing landscape. We need to shift our mindset and start thinking of ourselves as Chief Trust Officers.

With this shift, we can focus on how to increase trust with our customers and key constituents. My role at PayPal has helped to shape this philosophy, and I’d like to share four ways we can help to restore trust in this new world.

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A lot of risk management is about saying no, but true innovation happens when we can say yes Follow us on Twitter @RiskMinds

1. CROS SHOULD TAKE FIRST LINE OF DEFENSE RESPONSIBILITIES Security and risk are always a priority at PayPal – it’s in our DNA. After all, our very business is based on trust. It is a core component in the exchange of money between global buyers and sellers, and something I take very seriously. My team’s approach combines advanced security technologies such as strong authentication with riskbased analytics to help verify that customers are who they say they are.

People trust PayPal because we don’t share their financial details with everyone they transact with, and in the instances when fraud occurs, we have Purchase and Seller protections for eligible transactions. All these

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aspects of risk management are owned by the Risk team, which makes us accountable to our customers and helps strengthen their trust in PayPal.

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People trust PayPal because we don’t share their financial details with everyone they transact with, and in the instances when fraud occurs, we have Purchase and Seller protections for eligible transactions.

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2. BE INNOVATIVE, BUT STAY HUMAN

A lot of risk management is about saying no, but true innovation happens when we can say yes. To me, trust isn’t just about keeping bad guys out. We have to maintain high levels of protection without interfering with the actual customer experience. I want to enable the good guys to do commerce anywhere and across any device. That’s why we’ve combined advanced risk management and fraud prevention technologies with some of the brightest risk analysts in the world. We use our technology and knowledge of human behavior to quickly and accurately distinguish between the good guys and the bad guys.

3. MOBILE IS A RISK ADVANTAGE

As early as five years ago, even before mobile phones became the device of choice for our customers, we started preparing to manage risk on mobile devices. We recognized that there were some inherent security advantages to mobile devices. That personal connection to a person’s mobile device coupled with unique information like location data allow us to verify their PayPal account more effectively. Today, we use this type of risk-based analytics to create a One Touch checkout experience for our customers that doesn’t require usernames or passwords.

4. WE CAN’T DO IT ALONE

I don’t believe there’s a silver bullet when it comes to security, and I know

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As early as five years ago, even before mobile phones became the device of choice for our customers, we started preparing to manage risk on mobile devices

we can’t just build a big wall to stop people from getting in. PayPal is in 203 markets, and holds balances in 26 different currencies – we are truly a global company. With new threats being created every day, trust must be built between companies, customers and governments. I believe in maintaining relationships that will benefit our customers, and to this end, we have relationships with several financial partners.

Protecting customers takes a group effort. PayPal was a founding member of DMARC and of the FIDO Alliance. I believe we need to find new ways for people to authenticate – conveniently and securely – on their mobile devices. Industry collaboration is required to create a future where a password is no longer needed.

Trust has always been a central part of PayPal’s business and stands as a value that is an inherent element to the success of any company. It is imperative to keep moving forward, stay ahead of new risks and create genuine trust with customers.

TOMER BAREL Chief Risk Officer, PAYPAL Tomer Barel will be participating and speaking at the CRO Thought Leadership ThinkTank II: New Technologies, New Competitors, New Risks Examining The Changing Landscape For Financial Services at 2:15pm on Tuesday June 16.

Tomer will be available to speak with at the Meet the Speaker Lunchtables on the same day at 12:55pm.

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OPPORTUNITIES FOR BIG DATA IN CREDIT MARKETS Big data applications offer promise as the next generation of fixed income models, but success will depend on collaboration among software developers and financial engineers.

FINANCIAL MODELLING AND “BIG DATA”

Financial modelling involves translating a set of hypotheses market or agent behaviors into numerical predictions. The term “big data” describes applications involving large data sets to capture, curate, manage, and process data over relatively short time periods. As shown in Figure 1, big data systems have been characterized as having high volume, velocity, and/or variety of information sources.

In this brief report, I focus on potential applications of big data for models of default and recovery value in default. For example, a firm is said to be in default if misses or delays the payment of interest and/or principal; if it is in bankruptcy, administration, legal receivership, or other legal

block to the payment of interest and/ or principal. In default lenders rarely receive the full value of principal and the amount of face value recovered on defaulted bonds is called the “recovery value.”

The Merton (1974) model, depicted in Figure 2, has proven useful for estimating firms’ default probabilities. The theory views firms’ equity investors as owning a call option on the firm’s asset value (i.e., the debt plus equity value) with strike price at the level of the debt.

The firm’s bond holders are assumed to have made a risk-free loan to the firm, plus having sold a put option (i.e., the coupon spread above Treasuries) to the equity holders.

Given a firm’s debt level, equity price, and equity volatility one can estimate its default probability as related to the density of future asset

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Financial modelling involves translating a set of hypotheses market or agent behaviors into numerical predictions. values below the level of the debt (shaded portion of the distribution in Figure 2).

A simplified diagram of a recovery value model (Benzschawel and Su, 2013) appears in the top portion of Figure 3. There model has five major inputs: the current default rate, the bond’s seniority in the capital structure, and the obligor’s riskiness, industry, and geographical jurisdiction.

VOLUME

VARIETY

VELOCITY

VARIABILITY

• The quality of data that is generated is very important.

• This means that data from numerous sources must be brought together in Big Data applications.

• The term “velocity” in this context refers to the speed of generation of data or how fast the data is generated and processed to meet the demands and the challenges which lie ahead in the path of growth.

• This is a factor which can be a problem for those who analyse the data.

• It is the size of the data which determines the value and potential of the data under consideration and whether it can actually be considered as Big Data or not.

• The name “Big Data” itself contains a term which is related to size and hence the characteristic of volume.

• People with knowledge of the particular domain underlying a Big Data application must partner with developers to effectively use those data to their advantage.

Figure 1. Characteristics of Big Data

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• This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.

COMPLEXITY • Data management can become a very complex process, especially when large volumes of data come from multiple sources.

• These data need to be linked, connected, and correlated in order to be able to grasp the information that is supposed to be conveyed by these data.

RISKMINDSAMERIC A S To illustrate features of big data analytics, I use Thomson Reuters’ StarMine system (Roser, Bonne and Smith-Hill, 2013). As shown in the top portion of Figure 4, the system has three stages of processing: document analysis, component scoring, and aggregation. Overall, the system analysis the language in news releases, conference calls, corporate financials, and research.

It then creates scores from each source related to income statements, balance sheets, legal obligations, and external/market factors. It identifies words and phrases having the strongest relationship to the variable of interest and combines them into a single value.

I propose to use big data to improve inputs to existing models of default risk and recovery value. Several of these systems appear in the lower portion of Figure 4. The approach to both models is similar. For example, I envision having a big analyzer

estimate the inputs to default and recovery value models. For the Merton default model, the analyzer would estimate asset values of the firm at future horizons, the term structure of its leverage, and its implied equity volatility. Similarly, for recovery value, it would estimate a bond’s seniority, the firm’s credit state, its industry sector, and geographical jurisdiction. Then these inputs would be used in the standard framework and calibrated to historical default data.

Critiques of the big data paradigm come in two flavors; those that question the implications of the approach and those that question its execution (see Figure 5). In fact, there is very little in that table that has not been leveled against existing modeling techniques. Successful big data models will need to have reasonable inputs, rather than be “black boxes” and that same criterion exists for conventional techniques.

Also, modeling expertise, domain knowledge, and good judgment have always been highly desirable skills, and out-of-sample testing is a critical part of successful modeling. Finally, privacy concerns are not a problem unique to “big data” and laws restricting use of certain information will continue to evolve.

My experience suggests that our current models of default and recovery value are approaching asymptotic performance. Thus, I welcome attempts to apply big data methods to those problems.

Although many questions and challenges regarding big data applications remain, I suggest that benefits will accrue to those willing to take in challenges of incorporating big data methods, leaving late adopters at a disadvantage. One thing is evident; both our firm and our competitors are dedicating resources to find value in the data that exist within their firms.

Figure 2. The Merton Structural Model

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Figure 3. Model for Recovery Value in Default

Figure 4. Big Data Text Mining System Providing Input to Default and Recovery Value Models

Source: Thomson Reuters and Citi

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PARADIGM

EXECUTION

THE BLACK BOX ISSUE

BIG DATA HAS BEEN CALLED A “FAD”

• Big Data may not reveal much about the underlying empirical micro-processes that lead to solutions.

• A buzzword and “vague term”, but also an “obsession” with entrepreneurs, consultants, scientists and the media.

• In fact, it has been argued that Big Data will spell the end of the theory.

PRONE TO SAMPLE BIASES

NEED FOR DOMAIN EXPERTISE

• May neglect principles such as choosing a representative sample; too concerned with huge amounts of data.

• Big Data will always require domain knowledge for effective solutions.

• Data sources (e.g. Twitter) may not represent the population, leading to incorrect conclusions.

• Few employees have sufficient education, judgment and modeling skills.

DIFFICULT TO INTEGRATE ACROSS DATA RESOURCES (BIG DATA AND OTHER)

• Big Data needs to be complimented by Big Judgment.

• May pose formidable logistical as well as analytical challenges.

BACKWARD LOOKING • Others argue that such integrations are likely to represent most promising aspects of Big Data.

• Decisions based on the analysis of big data are inevitably ‘informed by the world’ as it was in the past (or current).

NO CLAIM ON OBJECTIVE TRUTH

• Combining big data approaches with computer simulations may predict outcomes of future scenarios.

• Working with Big Data is subjective, and what it quantifies does not necessarily have a closer claim on objective truth.

PRIVACY CONCERNS

• Big Data analysis is often shallow compared compared to analysis of smaller data sets.

• Privacy advocates are concerned about the threat to privacy represented by increasing storage and integration of personally identifiable information.

• Simultaneously testing many hypotheses is likely to produce many false results.

• Expert panels have released various policy recommendations to conform practice to expectations of privacy.

BIG DATA HAS FAILED TO DELIVER • Overstating flu outbreaks.

• Academy awards and election predictions solely based on Twitter were more often off than on target.

Figure 5. Criticisms of Big Data

REFERENCES

Benzschawel, T. and Su, Y. Recovery Value in Default: An Ensemble Model (E-3), Citi, December 3, 2013 Merton, R. A. On the Pricing of Corporate Debt: The Risk Structures of Interest Rates, Journal of Finance 29, pp. 449-470, 1974 Roser, R. Bonne, G. and Smith-Hill, C., 2009, StarMine Text Mining Credit Risk Model, StarMine white paper, January 23, 2013

TERRY BENZSCHAWEL Managing Director, Bond Portfolio Analysis, CITI INSTITUTIONAL CLIENTS GROUP Terry Benzschawel will speak on using big data in credit risk management at 10:50am on Wednesday June 17. He will also present on modeling sovereign risk and relative value at 2.00pm on Thursday June 18 and lead our Advance Credit risk Modeling Workshop on Friday June 19.

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