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UniversitĂ Cattolica del Sacro Cuore - Milano FacoltĂ  di Scienze Bancarie, Finanziarie e Assicurative Corso di Laurea in Banking and Finance

The Phantom Liquidity of Bond ETF

Xiaoou YE

under the supervision of Elena BECCALLI

submitted in partial fulfillment of the requirement of the Dual Master of Science in Banking and Finance / Mathematical Finance

February 2018


It's been one hell of a ride


Table of Contents

Summary ..............................................................................................................................v Chapter I. Introduction ........................................................................................................9 1.1

– Is Liquidity a concern for Bond ETFs?.....................................................9

1.2 – Classification of Exchange–Traded Products ...............................................11 1.2.1 – Fund Structures ..............................................................................14 1.3 – Fixed Income Market ....................................................................................16 1.3.1 – Bond Price Volatility Measures .....................................................18 1.3.2 - Fixed Income Liquidity ..................................................................21 1.4 - Bond ETFs .....................................................................................................22 1.4.1 - The Differences Between Bonds and Bond ETFs ..........................23 1.4.2 - Bond ETF Advantages and Drawbacks ..........................................23 1.4.3 – Bond ETF Liquidity .......................................................................25 Chapter II. The Exchange-Traded Funds Mechanism .......................................................30 2.1 – Market Participants .......................................................................................30 2.2 - Creation and Redemption Mechanism ..........................................................32 2.2.1 - Creation Unit Determination ..........................................................33 2.3 -Authorized Participants ..................................................................................34 2.4 – Two Layers of Market and Liquidity ............................................................36 2.4.1 - Primary Market Trading in ETF Shares .........................................37 2.4.2 - Secondary Market Trading in ETF Shares .....................................38 Chapter III. Liquidity Literature ........................................................................................39


3.1 – Econometric Liquidity Proxy........................................................................39 3.2 – Previous Literature ........................................................................................42 3.2.1 – ETF Liquidity under Inventory Model ..........................................44 3.2.2 – Liquidity between Price Discovery and Crowding-Out effect ......48 3.2.3 – Propagation of ETF shocks to Underlying Securities ....................52 Chapter IV. Sample ............................................................................................................55 4.1- ETFs Landscape .............................................................................................55 4.2 - Data Selection ................................................................................................57 Chapter V. Methodology ...................................................................................................59 5.1 – Low-Frequency Liquidity Proxies ................................................................59 5.2 – High-Frequency Liquidity Proxies ...............................................................62 5.3 - Model Setup...................................................................................................63 Chapter VI. Results ............................................................................................................69 Appendix ............................................................................................................................74 List of Tables .........................................................................................................75 List of Figures ........................................................................................................83 Model Proof ...........................................................................................................89 References ..........................................................................................................................91


Summary

In 2017 the global AuM industry confirmed the recent upward trend1, there was a steady inflow in the pool of traditional asset class, along with the exponential growth of passive investing. It is essential to notice that the disruptive technologies and investors preferences have already posed a threat to the active products and hedge funds managers.

In the next years, we will see a drastic change in the investing environment, especially in the distribution channel. Traditional asset managers have to seize their fees and brace actively on the advanced technologies such as Data Mining and Artificial Intelligence. Winning Players will include those transit quickly into advanced analytics to achieve superior investment performance. Traditional active assets will be squeezed, losing market share and revenue. Whereas alternative and passive instruments will continue to skyrocket in the growth of AuM and ultimately also in the revenues.

The Passive products, in particular, ETF are attractive investments products because of their low cost, high diversification and tax efficiency features. However, the actual reason for the popularity of these instruments relies on its accessibility for retail investors to gain exposure on the unconventional asset class or specifically designed investing theme.

There are no doubts that with the introduction of these new instruments, all market players are beneficial from the more personalization and transparency of the new products.

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Source: BCG analysis, HFR, BlackRock ETP report, IMA, Strategic Insight


Nevertheless, in the financial market, there is no free lunch, this availability of low-cost ETFs has same hidden costs and inevitably new related risks. Through a period in which equity markets have posted record highs and interest rates have been historically low, there have been relatively low opportunities for Fixed Income investors to identify profitable investment ideas. Recent inflow supports this tendency of FI investors moving to Bond ETF instruments.

In my dissertation, the analysis will focus on Bond ETF liquidity. A security is liquid to the extent that an investor can trade a significant amount of the security rapidly, at (or near) the current market price, and without bearing high transaction costs. As such, liquidity is a multidimensional concept. It is worth noting that due to liquidity discrepancy, ETF share price may not be aligned with the underlying securities fundamental value.

After reviewing the main features of Exchange Traded Funds and the related literature on liquidity. The empirical analysis starts with the selection of data sample (16 Bond ETFs) and period of study (2012 - 2017). The criteria for selection are adjusted to reflect the actual Bond ETFs landscape and the highly concentrated fund's sponsors.

The Bond ETF liquidity investigation is composed of two methodologies: Low / HighFrequency liquidity proxies and Regression analysis. By comparing the result of these two model, we can verify the extent of liquidity of Bond ETFs in Europe and in the US.


The low-frequency proxies are advantageous because they are more flexible to the study of liquidity over relatively long-time horizons. However, they are limited because they do not directly mirror actual trading processes, while the high-frequency measures do. Thus, high-frequency liquidity proxies are often used as benchmarks to determine the best low-frequency proxy. This is not a general rule; however, because each measure captures a different dimension of liquidity, it may be a good compromise to combine the data.

The results derived from the computation of econometric proxies are evaluated via correlation analysis. Among the seven low-frequency liquidity proxies, I find that the Percentage bid-ask spread and Amihud (Yearly) measures are higher correlated with the high-frequency measure.

By collecting the High-frequency data of two recent market distressed days, we compared the granular data with the end-day reported data to verify the actual Bond ETF liquidity. European Bond ETFs display a positive premium. Investors who want trade European Bond ETFs in these days have to pay an additional liquidity component (the range goes from 4 bps to 369 bps) to enter into a position. Among US Bond ETFs we do not find significant liquidity premium.

By using the inventory risk model developed by Stoll (1978) and then extended by Calamia et al. (2016) for their study in the equity ETF. We developed a simple model that considers for the underlying bond securities characteristics and the EFT mechanisms.


The liquidity of Bond ETFs instruments can be broken down into the sum of two main components: the volatility of the underlying index and the AP incentive to take part the creation and redemption process to keep the ETFs share price close to their NAV. The result of the regression analysis illustrate that the volatility of the benchmark index has a negative impact on the Bond ETF spread (more creation/redemption activities lead a tighter spread) and the price Discrepancy (AP’s incentive for the traded ETF) is significant only in the Corporate Bond (both Investment Grade and High Yield) ETFs segment, thus AP incentive to actively participating the arbitrage mechanism is limited to certain Bond categories. By combing the results our analysis, we have the clues to support that, expect for aggregate Bond ETFs, Bond ETFs liquidity is not continuously provided by the AP across segments and neither constant during market distress circumstances.

My contribution to the argument is limited due to the lack of data and means, but I hope that it can be seen as a starting point and an encouragement for future discussion on this subject.


Chapter I. Introduction

In this chapter, the primary emphasis will be on the analysis of the fixed income exchange-traded funds, given the popularity of the asset class and the exponential inflow in term of AuM. To fully understand the characteristic of this product, we have to decompose the Bond ETF instrument to present its hybrid features. After reviewing all elements, we will finally display how Bond Exchange-Traded Funds works.

1.1 – Is Liquidity a concern for Bond ETFs?

The degree of liquidity in the Fixed Income market has declined since the global financial crisis. The reduced dealer participation (due to banking industry financial regulation2 designed to reduce risk) and an increase of passive Fixed income instruments have contributed to the deterioration of this sector.

New regulations, principally higher bank capital requirements and limitations on propriety trading have reduced the inclination and capability of banks to deliver liquidity to the fixed income markets. The new era of lower liquidity in FI market is likely to

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Regulation include: higher bank capital requirements, limits on balance leverage and limitation on property trading.

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continue, and so our approach to asset allocation and portfolio construction3 should change and adapt it to the new environment.

Liquidity in the bond market is problematic to measure and one metric is not enough to explain the multidimensional concept. Low yield and fast growth of Bond ETF have led to a significant expansion in the FI markets in the last ten years. However, turnover4 has decreased. This indicates that liquidity has not kept pace with the overall growth of the AuM. Data also exhibit that the vast majority of the trade volumes are occurring among the most significant, most recently issued securities, whereas “off-therun� securities have become much more difficult and expensive to trade.

Before the financial crisis, banks had historically been the largest risk-taking and liquidity providers during periods of market stress. Dealers helped bond market liquidity by retaining large positions (inventory) for their proprietary trading portfolios and facilitating trades between investors. However, under the new regulatory agenda, the role of primary dealers has become less profitable and more challenging, so the willingness of dealers to assume risk have fallen considerably.

Limited dealer balance sheets will have severe implications in times of market stress. Under the new supervisory framework, it is becoming more difficult for dealers to be able to take advantage of trading opportunities. Previously, banks would have been willing and

3 4

less liquidity can result in higher trading costs and challenges in sourcing appropriate bond the percentage of the market that trades in a given time period

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able to help absorb excess inventories in a market backup, providing a cushion for revaluations. More limited dealer participation is likely to effect in price corrections and unnecessary volatility during times of increased risk aversion.

Fixed income ETFs represent baskets of securities. ETFs allow investors to diversify their risk efficiently and can consume intra-day liquidity. In some respects, the rise of ETFs has improved liquidity by permitting traders to make markets in other fixedincome securities, because they can be an effective hedge.

However, ETFs have also complicated the bond market considerably and introduced new risks which have not been studied at systematical level. For instance, in the case of massive redemptions or other stressed-market environments, the effectiveness of the create/redeem mechanism or the hedging instruments used in trading ETFs could fade.

The ETF quoted price may not reflect the actual prices realized on the bonds in the baskets of reference securities. Creation and Redemption mechanism could face the issue with the availability to trade a basket of bonds in the OTC.

1.2 – Classification of Exchange–Traded Products

There are several different product structures that investors refer as ETF, but that do not meet its exact definition. It is crucial to distinguish between the core products and what kind of risks are inherent in each instrument.

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Base on the Blackrock recommendation5, the world largest ETF issuer, the classification scheme for exchange-traded Products can be divided into five categories. [Table 1]

The Exchange Traded Product (ETP), refers to a broad category of investment securities. This include funds, limited partnership (LP), index-linked notes, and trust listed and traded on the secondary market. The common characteristics of ETP are:

- ETF sponsors do not have a direct relationship with investors. They issue creation units (typically 50000 shares or more) to Authorized Participants (APs). The process is known as creation units. - The creation and redemption process is done principally through In-kind exchange mechanism. The fund sponsor in exchange for a predetermined list of securities will deliver the ETF shares to the AP. The mechanism allows the ETF to avoid selling securities to raise cash to meet redemptions and it prevents capital gains distributions. Additionally, the fund manager can also obtain the lowest cost basis per transactions, further reducing the ETF’s tax burden. - The ETP shares are traded throughout the day on a public exchange. The Market liquidity is provided by AP who utilize the creation and redemption feature to exchange underlying baskets for ETP shares or through the secondary market via supply and demand of the public exchange.

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Viewpoint - Bond ETFs: Benefits, Challenges, Opportunities, July 2015 – BlackRock

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The Exchange-Traded Fund (ETF): like mutual funds, in that, they offer public investors a collective interest in a pool of financial investments and other assets, but shares can be bought and sold like stocks on an exchange. Thus, they can be lent, shorted, margined or subjected to any other strategy used by sophisticated equity investors. ETFs have several distinguishing characteristics:

- The portfolio is managed by an investment advisor with a mandate to track the index or to beat a portfolio benchmark. If it is index-based, the portfolio manager has discretion on the way to track the index (full replication versus sampling of the index, market weights vs. equal weights). Like traditional mutual funds, ETFs commonly have an independent board of trustees that has oversight over the fund. - In U.S. ETFs are registered under the Investment Company Act of 1940, which provides investors with specific regulatory protections. In Europe, almost all ETFs listed on the stock exchange are UCITS funds, which also protect retail investor interests by focusing the regulation on the fair competition and effective governance measures. •

Exchange-traded note (ETN): A debt security issued by an underwriting bank to track the return of a specific underlying benchmark. The debt is typically senior, unsecured, and unsubordinated. ETNs have a maturity date like most other debt securities. The main identifying characteristic is that an ETN is backed only by the credit of the issuer, typically an investment bank.

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Exchange-traded Commodity (ETC) refers to funds that hold physical commodities such as gold or any other precious metals. Unlike an ETF, shareholders have direct ownership of the assets underlying the ETC and do not have the protections associated with ownership of an ETF share.

Exchange-traded Instruments (ETI) have particular structural features that aim to take opposite position or overweight some assets. The fund often obtains leverage through swaps. This category includes for the most part inverse or leveraged funds.

1.2.1 – Fund Structures Traditional Open-end mutual funds, Closed-end funds, Unit Investment Trust and Exchange-traded funds are often referred to generally “funds”; they operate under a similar regulatory framework. However, they differ in several crucial ways. Since the structures of these funds are often confused, [table 2] outlines the crucial differences and similitudes. The fund types have a different mechanism for establishing prices at which share transactions occur and for providing liquidity to investors.

In a traditional Open-End Fund (most Mutual Funds), demand for shares of the portfolio is satisfied through an end-of-day subscription and redemption process. Individual investors interact directly with the fund, based on the terms in the fund’s prospectus, Investors can exchange shares at the end of the day at the fund’s best estimation of net asset value (NAV). As more investors subscribe to the fund, its assets increase as do the number of shares outstanding. Likewise, redemptions reduce the fund’s assets and the

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number of shares outstanding. Consequently, when there is a significant imbalance between buyers and sellers, any cost derived from the outflow from the fund are covered by all remaining investors.

In a Closed-End Fund, investors buy and sell shares on the exchange intraday. Since the size of the fund is fixed both in the number shares outstanding and assets holding. Secondary market supply and demand determine the price at which shares are bought and sold. Therefore, CEFs may trade at premiums or discounts to the value of the underlying securities basket. Any imbalance affects the exchange price but does not result in purchases or sales of holdings by the fund. In a closed-end fund, there is no mechanism to reconcile differences between the market-determined price and NAV; the exchange prices commonly exhibit premiums and discounts to NAV.

A Unit investment trust (UIT) is an investment company that offers a fixed portfolio, generally of stocks and bonds, as redeemable units to investors for a specific period. It is designed to deliver capital appreciation and dividend income. A UIT typically will make a one-time "public offering" of specific and fixed number of units (like closedend funds). Many UIT sponsors, however, will maintain a secondary market to manage the liquidity and market appetite for the product. A UIT does not actively manage its investment portfolio. That is, a UIT buys a relatively fixed portfolio of securities and holds them with little or no variation for the life of the UIT. As the composition of the investment portfolio of a UIT is static, investors know what they are investing, and they can find the portfolio securities held by the UIT listed in its prospectus.

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Exchange-traded funds combine characteristics of both closed-end funds and traditional open-end mutual funds. Like a CEF, ETF shares can be bought and sold on the exchange intraday. However, ETFs has a creation and redemption mechanism for keeping the market price closer to the NAV by adjusting the supply available shares based on investor demand. Like an open-end mutual fund, ETF shares can be created or redeemed at the end of the day (the fund can grow or shrink, based on end-investor demand). There are two relevant differences between how this process works in an ETF versus an openend mutual fund. First, in an ETF, these end-of-day primary trades are facilitated by a preapproved group of institutional firms, known as APs, who have entered into a nonbinding agreement with the ETF’s distributor. Second, in many ETFs, primary trades transactions happen in-kind and do not require securities purchases or sales by the ETF. APs deliver a basket of securities to the ETF in exchange for ETF shares. Most active APs will also act as agents to facilitate creations or redemptions on behalf of their clients. These activities could be on behalf of the third party (e.g., market makers) who regularly provide two-sided quotations to clients or for end-investors seeking to access primary market liquidity. The roles of APs and market makers are distinct. That said, in the actual setting, most firms are both an AP and a market maker in the same ETF instrument.

1.3 – Fixed Income Market

Bonds are versatile instruments; they provide safe, steady and predictable returns that have low correlations to common stocks, making them an exceptional way to balance

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higher-risk equities in a portfolio. However, for the average investor, investing in individual bonds is difficult and sometimes next to impossible.

Poor market transparency: Bonds trade over-the-counter (OTC), meaning there is no single exchange on which they trade and no official agreed-upon price. The market is problematic to negotiate, and investors may find they receive widely different prices from different brokers for the same bond.

High markups. Broker markups on bond prices can be substantial, especially for smaller investors.

Poor liquidity. Bonds vary widely in their liquidity. Some bonds trade daily, while others only trade weekly, or even monthly—and that is when markets work perfectly. In times of high market volatility, some bonds may stop trading altogether.

Fixed-income securities are a mainstay of investor portfolios. Bonds and other fixed-income instruments are simple in principle—they are debt issued from an institution that needs money to the public. Bonds investors are the lenders and bond issuers are the borrowers. Investors who lend the money expect to be repaid; their compensation is made by the full repayment of the principal and the interest on the loan (often takes the form of a regular coupon payment). The fact that bonds provide a steady cash return and eventually repay the full original capital (assuming all goes well) gives them a unique role in a portfolio—they provide a steady flow of cash with lower volatility than equity. Historically Fixed Income has been

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used as a counterbalance to equity investments for another reason: Historically when stocks go down, bonds often go up.

1.3.1 – Bond Price Volatility Measures In general, bond values decrease when interest rates increase because investors can get a better deal in a rising-rate environment. For instance, a bond issued at par and pay a five percent interest, if the interest rates suddenly increase, a newly issued bond that is very similar to the original bond might carry a six percent coupon. Since the yield is fixed at issuance, the only feature that can change is the price, in our case, the market value of the five percent coupon bond would decrease to make it comparable to the higher yield of the new bond.

The price volatility of a bond is positively related to time to maturity (T) and negatively related to coupon and yield (r). All else equal, a 10-year bond carries more interest-rate risk than a 5-year bond. This makes sense because the money invested is subject to the risk of rising interest rates for a more extended period. Keeping all other parameters fixed, given a higher coupon payment or a higher yield, changes in price is smaller. We can assume that the size of the coupon payments affects the weighted average of the cash flow of the bond directly.

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(1)


In finance, "similar" bonds are not identical, and different bonds will react very differently to the same shock in rates. The most common statistic proxies used to measure the price volatility are duration and convexity.

Duration is the price sensitivity or volatility measure to changes in yield. A higher duration (time-weighted) means more interest-rate risk, and it will take longer for the investor to get paid for a 30-year bond than with a 10-year bond. Duration is an estimate of the change in a bond's value in response to an overall change in interest rates. A change in the interest-rate environment can significantly affect the value of a bond or portfolio of bonds—such as an ETF. Duration provides a way to measure rate risk.

This measure allows investors to directly compare interest-rate risk across bonds, bond portfolios and bond ETFs. The two most common duration measures are Macaulay duration [equation 2] and Modified Duration [equation 3].

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(2)

(3)


For small changes in y, duration measures will give us % change in price and dollar change in price that are very close to the actual changes. While for substantial changes in y, Duration overestimates (underestimates) negative (positive) change in price and therefore underestimates the new price.

There also some drawbacks of using Duration: 1) it is a forward-looking and linear estimate of a bond’s value and interest rates. 2) underlying assumption that the interest rates change by 1% uniformly across all maturities.

Convexity is the measure of the curvature of the price-yield relationship. It reflects the relationship between duration and y, in other words, it tells us how the sensitivity of price varies with changes in interest rates. By using both duration and convexity, we can obtain a better estimation6, given a delta y, the new price of the bond.

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(4)

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(5)

Example: 9% coupon, 25-year bond, y = 9% and P = $100. MD = 9.88, Convexity = 160,72 When y goes from 9% to 12%. Estimated % change = % change due to duration + % price change due to convexity = -29,64% + 7,23% = -22,41% (compared to actual -23,64%)

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1.3.2 - Fixed Income Liquidity The bond market is undoubtedly less liquid than the stock market. Many bonds rarely trade, sometimes, it happens that some bonds can be out the trading market for weeks or even months. The new regulation introduced in the wake of the 2008 financial crisis has dried up the fixed income liquidity even further leading less incentive for banks to maintain deep inventories and in particular to retain fixed-income assets.

Fixed income market liquidity plays a vital role in the stability of the financial system and the conduct of monetary policy. Therefore, Central Banks have a keen interest in monitoring liquidity conditions as well as the drivers that affect their robustness during episodes of market stress. Market liquidity can be roughly defined as the ability to rapidly execute large financial transactions at low cost with limited price impact. Assessing liquidity conditions, therefore, requires measures of immediacy, tightness, depth, and resilience as well as indicators of market breadth if comparing liquidity across similar instruments. Such measures are difficult to come by and are typically only available for markets where trading takes place on central limit order books and is subject to comprehensive data collection and disclosure requirements. (i.e., those markets that are usually considered to be the most liquid: equity exchange platform).

Traditionally, Fixed income market is characterized by bulky players and infrequent trading activities. These markets have broadly maintained a dealer-centric framework, where traditional market makers provide immediacy services by warehouse assets to meet client’s orders or acting as brokers by matching orders. Recent structural

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changes (technology in the first place) and monetary policy developments are likely to transform significantly fixed income markets across different jurisdictions. This is particularly the case for markets segments that still rely heavily on principal-based marketmaking services, such as those for corporate bonds. Based on this reasoning, sizeable and permanent reductions in dealer inventories may indicate a diminished capacity and willingness of market-makers to provide risk capital.

Bond issuance has hit new records since the global financial crisis as borrowers take advantage of low-interest rates, leading to amplified market fragmentation as the same corporation may issue numerous of unique securities. This trend, tied with transaction sizes and lower turnover in the over-the-counter bond market, has led to discussions of potential innovation to improve the fixed income markets. The Rising demand for immediacy is shifting the Fixed income paradigm.

1.4 - Bond ETFs

A bond ETF is a fixed income investment in a stock like a wrapper. A bond ETF tracks an index of bonds and tries to replicate its returns. Though these instruments are debts, they trade on an exchange like stocks, giving them some attractive equitylike properties.

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1.4.1 - The Differences Between Bonds and Bond ETFs Bonds and bond ETFs may embrace the same intrinsic investments, but the fact the latter instruments are traded in a public exchange, they may deviate from the traditional fixed income frameworks.

•

Bond ETFs do not mature. Individual bonds have a fixed, unchanging date at which they mature, and investors get their money back. Bond ETFs, however, maintain a constant maturity, which is the weighted average of the maturities of all the bonds in its underlying portfolio. At any given time, some bonds of the underlying basket may be expiring or exiting the age range that a bond ETF is targeting. As a result, additional bonds are continually being bought and sold to keep the portfolio's maturity approximately near the target.

•

Bond ETFs pay out monthly income. One of the bonds' most significant benefits is that they pay out interest to investors on a steady schedule. Usually, these coupon payments happen every six months. However, bond ETFs hold many different issues at once, and at any given time, some bonds in the portfolio may pay coupon to the bondholder. Therefore, bond ETFs usually pay interest monthly, rather than semiannually; the value of this payment can vary from month to month

1.4.2 - Bond ETF Advantages and Drawbacks Bond ETFs offer many advantages over single bonds: First, Diversification: An ETF leads the ownership of hundreds or thousands, of bonds. By investing an index, the purchase price is significantly lower than investing in each issue individually. Second, Ease

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of trading: Investors can buy and sell bond ETFs from their regular brokerage account without the need of any professional intermediaries. Third, Price transparency: With a bond ETF, there is a daily publicly list of the underlying assets. Lastly. Bond ETF provides a more regular income. The amount of coupons payment may fluctuate from month to month; monthly payments give bond ETF investors a more regular cash flow to use or reinvest.

There are also two main downsides to bond ETFs. 1) The investors are not guaranteed to get their money back. Because bond ETFs never mature, they never offer the same protection for the initial investment the way that individual bonds can. 2) Since Interest rates change over time, if it rises, the value of bonds may fall, and the selling pressure can cause depreciate the initial investment. With individual bonds, the risk can be mitigated by just holding on to a bond until maturity, when there is the full-face value repayment. Since Bond ETFs do not mature, Investors have to deal with this risk.

Compared with stocks—like those in the S&P 500, which trade throughout the day on the NYSE and Nasdaq—bonds are relatively illiquid, and their actual price is harder to know with certainty. For Instance, shares of Amazon are fungible, so the last price at which a share was traded is a valid proxy for the current value of every Amazon share. The bond market is different.

First, bonds trade much less frequently than stocks—so the last traded price might not be current at all. Second, they do not trade on an exchange: Most bond trades are individual 24


"over the counter" agreements between two parties. Third, bonds come in much greater diversity than stocks; for instance, Corporate X may have many bond issues, each with different yield and maturities and each instrument has its price (while, for the equity market, one stock represents one company). Fourth, ETF issuers rely on bond pricing services for "fair" value estimations of their holdings; these estimations are based on the current belief and price expectation that the fund might receive if they sell bonds immediately. Obviously, in case of fire-sale, the price will always be less than what investor expect to receive, so there's a "natural" distortion in the reported NAV of all bond ETFs. For all of the previous reasons, it is common that a highly liquid bond ETF can serve as price discovery for the actual fair value of the basket of bonds it holds. In other words, the quoted price of the bond ETF can be a better approximation of the aggregate value of the ETF's underlying bonds than its NAV. Therefore, substantial premiums and discounts do not necessarily signal any mispricing in the ETF. At the same time, we cannot completely ignore it. ETF share prices are not always in line with their NAV. Sometimes significant premiums and discounts signal that the ETF itself trades poorly and is, therefore, a lousy price-discovery vehicle.

1.4.3 – Bond ETF Liquidity ETFs have two layers of liquidity: primary and secondary. Primary liquidity (Creation and Redemption) is the liquidity of the primary market for the ETF, while secondary liquidity is the liquidity of its secondary market (Exchanges). [Figure 1]

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Primary liquidity is the process to create or redeem ETF shares. The supply of ETF shares is flexible. APs can create or redeem ETF shares to balance demand. To create new ETF shares, an AP submits the requested basket of the underlying securities—a creation unit—to the fund sponsor, which give an equally valued basket of ETF shares in return. To redeem existing ETF shares, an AP submits a creation unit's worth of ETF shares and receives an equally valued basket of the underlying securities. Primary liquidity, therefore, deals with how liquid the underlying securities that an ETF holds are. This liquidity dictates how efficiently APs can perform their job.

Primary liquidity does not solely affect secondary liquidity and vice versa. In the secondary market, liquidity is mainly determined by the trading value of the ETF shares. In the primary market, however, liquidity is determined more by the value of the ETF's underlying securities, since APs and issuers use those to create and redeem ETF shares.

Secondary liquidity is "on screen" liquidity. Retail investors trade ETFs in the secondary market; Private investor interacts with another player or with a market maker using ETF shares that already exist. The secondary liquidity can be valued by looking at premiums and discounts to net asset value (NAV), bid-ask spreads as well as average volume. The underlying bond market does not wholly dictate bond ETF liquidity. The liquidity of the bond market (the ETF's primary liquidity) is only part of the puzzle. A bond ETF's liquidity depends both how much Authorized Participants are willing to engage in the creation and redemption mechanism and how it trades in the secondary market. On average, 77 percent of the total daily activity in Bond ETFs occurs on the secondary

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market7. However, the primary liquidity is still significant. For instance, it determines the premiums/discounts of ETF in the secondary market:

A highly liquid portfolio of bonds inside an ETF with low trading volume, it may be problematic for a retail investor to trade the low ETF shares available. However, since the underlying securities are liquid, an AP can create/redeem a creation unit of shares easily upon the market demand.

A highly illiquid portfolio of bonds inside an ETF with high trading volume, it may be easy for a retail investor to trade ETF shares. However, an AP may find it extremely difficult and expensive to gather the underlying bonds for a creation unit.

When is hard for an AP to collect the underlying basket securities, It is more likely that AP has to born more transaction costs, it will eventually lead to ETF trade a premium to its market price. Likewise, if the underlying bonds are illiquid and the investors want to liquidate their position, AP will let ETF trade at a discount to NAV. Though most retail investors can focus on secondary, "on screen" liquidity, the liquidity of the underlying market is always significant.

Bond ETFs offer a solution to this current “the search for the yields� environment (dovish interest rate policy), and investors demand of transparency and immediacy for fixed income. Bond ETFs typically allow investors to redeem their shares at short notice,

7

Investment Company Institute Factbook 2017

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which, in turn, suggests that fund managers rely on dealers’ immediacy services to sell bonds promptly. Rather than trade thousands of unique bonds, investors can gain exposure to a fixed income market segment by merely taking a position in a bond ETF that represents that sector. By concentrating trading demand in a single instrument that trades continuously with centrally-reported quotations (e.g., stock exchange), bond ETFs help buyers and sellers of bonds find each other efficiently without having to rely on OTC dealers. Theoretically Both institutional and retail investors in bond ETFs benefit from this, as ETFs provide incessant transparency, instant diversification, intraday liquidity, and lower trading costs, resulting in quick growth in bond ETFs in recent years. Since liquidity in the bond market has dried out, many ETF investors reasonably have begun to have concerns about the liquidity risk based on those bonds.

Recent debates about bond ETFs often refer to a “liquidity discrepancy” between the underlying bond security and the more liquid bond ETFs. The concerns mirror the intersection of two recent trends: the mirage of growing liquidity challenges in fixed income markets alongside the exponential growth of assets in bond ETFs. As stated before, the term liquidity is a multidimensional concept; it is often used as a catch-all phrase for several notions, which has created confusion and led to the conflation of distinct issues.

It is essential to begin by distinguishing between “structural liquidity” and “market liquidity”. Structural liquidity refers to the fund structure; it includes redemption

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frequency, redemption provisions and the ability to make redemptions in-kind. Market liquidity refers instead to the ability of investors to trade assets without unduly large price movements, whereas structural liquidity refers to the structural features of a fund that determine how often and under what conditions shareholders can redeem. In financial stress conditions, overall market liquidity can be harshly challenged. In these episodes, there may be a “liquidity discrepancy� between the liquidity of its underlying holdings and ETFs instrument.

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Chapter II. The Exchange-Traded Funds Mechanism

ETFs is traded both in the primary and secondary market. Authorized Participants activity should keep the ETFs price close to their NAV. Although APs can benefit from opportunities generated by the arbitrage mechanism, they are not obligated to engage in any all transactions with the third party.

2.1 – Market Participants

ETFs have the exceptional advantage of interacting in both the primary and the secondary markets. The primary market is where the ETF undertake their process of creation and redemption mechanism. The primary market activity is directly related to the underlying basket. Creations and redemptions occur at net asset value (NAV), and consequently, it is essential to understand this mechanism to have smooth and efficient transactions in the secondary market. In the exchange market, trading takes place separately from the underlying basket. The ETFs can trade at prices that are dictated by market forces. The link between the two markets is the arbitrage opportunity that is allowed by ETF sponsor to facilitate the liquidity and to attract new investors. [Figure 2]

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The primary market of the Exchange-Traded Funds is made up of three main players:

•

ETF Sponsors: The issuer of an ETF is the fund manager who manages the ETF and its underlying securities. The issuer publishes a basket of securities for delivery each trading day to APs and swaps this basket when delivered to ETF units.

•

Authorized Participants: or APs are authorized trading participants that have an agreement in place with the ETF issuer to create and redeem ETF share in exchange for its underlying basket. For the Creation mechanism, the required basket for delivery is published every day by the issuer and reflects the investments and value of the underlying fund. AP delivers a basket of securities rather than cash in exchange for an equal value of units in ETFs. Redemptions take place via a similar method; ETF issuer delivers the fund shares in exchange for a basket of securities of equal net asset value.

•

Market Makers: the role is slightly different from AP. They offer liquidity to the market participants by quoting buy and sell prices throughout the trading day. The Market makers primary function is to provide continuous liquidity to the market. The process begins with the issuer distributing the current fund composition to the market every morning, allowing market makers to price the basket of securities underlying the ETF. Market makers place a quoted spread around the true value of the ETF and send these prices to the stock exchange as orders. Market maker orders are updated uninterruptedly throughout the day to reflect price changes in the underlying securities. Often, institutional participants in the market can fulfill both the authorized participant and market making roles.

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The secondary market is made up of more heterogeneous players in the stock exchange. Different parties may include: Securities Exchange; Financial Advisers and Brokers; Investors; Share registrars and other minor participants. [Figure 3]

Financial advisers and brokers can trade ETFs on behalf of their clients through two types of brokerage services: non- advisory brokers - which include direct orders through an administration platform or online broker, and, full-service brokers who can offer advice and guide an investor or their adviser through the ETF transaction process.

As ETFs are quoted investments, a share registrar manages the administration for investors such as paying distributions, providing distribution and allowing investors to select the distribution reinvestment plan.

2.2 - Creation and Redemption Mechanism

Creations of new shares and redemptions of existing shares are typically originated by market makers who involve an AP when there is an imbalance between supply and demand of ETF shares that cannot be absorbed through the secondary market

ETFs offer transparency, which is crucial to the pricing of the ETF and the creation and redemption of shares. Before the opening of each business day, an ETF sponsor publishes current fund holdings and the basket of securities that the ETF sponsor will accept for creations or deliver for redemptions for such trading day. For ETFs based on

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physical securities consisting of stocks or bonds, the transactions between an ETF and an AP are typically in-kind where the AP delivers or receives a basket of securities identical to the ETF’s holdings. [Figure 4]

2.2.1 - Creation Unit Determination The average trading volumes of the underlying security of an ETF basket will be determinants in the potential future volumes of the ETF. At each step of determining the underlying universe and whittling it down to the ETF creation basket will be some form of analysis of its underlying constituents. If the ETF structure is not exploited, there is potentially some more flexibility in the liquidity of the underlying baskets. Closed-end funds (CEFs) gained some of their popularity from their ability to invest in less liquid assets because they do not have a daily issuance component. The ETF wrapper allows for the daily issuance and redemption of shares; liquidity in the underlying basket is vital to facilitate these transactions.

Most of ETF rebalance regularly their underlying basket. The volume and the liquidity of the underlying components influence the determination of the ETF holdings. This is especially true for Bond ETFs since fixed income instrument has a maturity date, the fund manager has to manage the fund composition to minimize the tracking error and at the same time to assess the risks related each underlying asset.

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2.3 -Authorized Participants

An authorized participant (AP) is typically a large financial institution that enters into a legal contract with an ETF distributor to create and redeem shares of the fund. In addition, APs are self-clearing broker-dealers that can process all required trade submission, clearance, and settlement transactions on their account. [Figure 5]

When an Authorized Participant (AP) does a creation, the requisite shares matching the creation unit are delivered to the ETF sponsor, along with the required cash component, and the sponsor delivers the AP shares of the ETF. The issuer does not maintain an inventory of shares that it delivers to the AP, but as part of the creation process, the sponsor “issues� new ETF shares. These new shares are reflected in the shares an outstanding number of the ETF that is published daily. In an opposite situation, when the AP processes a redemption order, shares of the ETF are delivered to the issuer, and the issuer delivers the underlying holdings basket to the AP. The issuer does not hold onto those shares or put them in some inventory the Assets under Management of the ETF would decrease.

APs play a significant role in the primary market for ETF shares since they are the only investors that can interact directly with the fund sponsor. APs do not receive compensation from an ETF issuer and have no legal obligation to create or redeem the ETF’s shares. APs typically derive their compensation from acting as dealers in ETF shares and operates in the primary market when it is more efficient or convenient to manage their aggregate exposure than trading in the secondary market.

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Some APs are also clearing brokers and receive payment for processing creations and redemptions as an agent for third parties such as registered investment advisers and various liquidity providers, including market makers, hedge funds, and proprietary trading firms. Some APs also play another role in the ETF ecosystem by acting as registered market makers in ETF shares that trade on an exchange.

Delivering and receiving in-kind shares is a process whereby the AP acts as the execution-and-trading agent of the underlying shares. It is the responsibility of the AP to either purchase the shares in the market or borrow those shares to deliver to the issuer. The in-kind shares exchange is acceptable because the shares are fungible vehicles, interchangeable for each other.

This process removes the expense of trading from the ETF when there is a growth or decrease in assets. It also enables the ETF to attain a high level of tax efficiency because it can divest its portfolio of shares without trading them in the marketplace and generate a taxable event. Because of this ability, capital gains distributions are typically very low or nonexistent in many ETFs. By contrast, a mutual fund that is facing the redemption of a relevant seller of assets will be required to go out and sell assets from the fund so that it can deliver cash to the redeemer. Mutual fund outflows can generate trading expenses to the fund and also generate taxable events in the form of gains from securities that have been bought and must now liquidate in the marketplace. In the ETF structure, most trading expenses due to asset growth and shrinkage are the responsibility of each investor and are not borne by all shareholders.

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AP activity can be seen as an automatized adjustment of the outstanding shares of the ETF in response to the market volatility, thereby benefiting fund investors through lower costs. For instance, if a large institutional investor seeks a large chunk of a specific ETF’s shares, it may go to an AP to have easier access to the creation process. The purchaser of ETF shares delivers either cash or securities or both component to the AP, who sequentially delivers the basket of securities to the ETF sponsor, who issues ETF shares to the AP to give to the ETF buyer. The arbitrage mechanism encourages APs and their clients to provide offsetting liquidity when there is an excess of buying or selling demand for ETF shares. Although market makers will benefit from any possible arbitrage opportunities, they are not obligated to enter the market, and no agreement force them to do so.

APs are often large financial institutions or more specialized market makers. [Table 3] The principal role of the AP is to facilitate and smooth the arbitrage mechanism, inventory and expertise in the trading the underlying securities is an essential qualification of an AP for an ETF segment. The ETF sponsor determines which APs are authorized to transact with the ETF before launching the ETF.

2.4 – Two Layers of Market and Liquidity

The price of an ETF share on a stock exchange depends on numerous factors, most of them are beyond the forces of market dynamics. Imbalances in the market forces can

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lead deviation of the ETF price from its underlying value. Although substantial deviations tend to be short-lived for many ETFs, Primary and Secondary Market have entirely different setting and dynamics.

Typically, retail and smaller institutional traders will purchase or sell securities on a trading venue or exchange, either interact with each other directly or through intermediaries such as market makers or other liquidly providers. When ETFs are traded on an exchange, they are considered to be trading in the secondary market. The primary market is one of issuance. Similar to an initial public offering (IPO), shares initially are issued in the primary market, and they begin trading in the secondary market.

2.4.1 - Primary Market Trading in ETF Shares The ability to create or redeem ETF shares at the end of each trading day supports the ETF structure; trades are made at market prices that approximate the underlying market value of the portfolio. When a deviation occurs, APs (for their own account or on behalf of the third party) may create or redeem creation units in the primary market to capture a profit. For instance, When an ETF is trading at a premium (underlying basket is underpriced), market participants may find it lucrative to buy the underlying securities while simultaneously short selling the ETF. At the end of the trading day, the APs will deliver the creation basket to the ETF in exchange for ETF shares that are used to cover the ETF short sales. The same mechanism happens when an ETF is trading at a discount (underlying basket is overpriced), market participants may find it profitable to sell short the underlying basket and buy the ETF shares. APs deliver ETF shares to the fund in 37


exchange for the ETF’s redemption basket, which is used to cover the short positions in the underlying assets. This process is commonly described as arbitrage, help keep the market-determined price of an ETF’s shares close to its underlying value.

2.4.2 - Secondary Market Trading in ETF Shares ETF investors trading in the secondary market do not interact with the ETF sponsor directly and do not create transactions in the underlying securities. Although many large institutional investors can access ETFs in both the primary and secondary markets, most retail investors only access them in the secondary market. Most ETF investors are trading in the secondary market are not motivated by arbitrage. [Figure 6]

Across all ETFs, investors make more extensive use of the secondary market (trading ETF shares) than the primary market (creations and redemptions of ETF shares through an AP). On average, 89 percent of the total daily activity in ETFs occurs on the secondary market. Even for ETFs with narrower investment objectives—such as emerging market equity, domestic high-yield bond, and emerging market bond—the bulk of the trading occurs on the secondary market (95%, 79%, and 73%, respectively). On average, secondary market trading is a smaller proportion (77%) of total trading for bond ETFs than for equity ETFs (90%).

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Chapter III. Liquidity Literature

Liquidity is a multidimensional and elusive concept. In this chapter, I review the statistical based measures (using either low-frequency and high-frequency data) that researchers have developed to capture one or more dimension of a security’s liquidity. The section is completed by a quick review of the previous literature on market liquidity.

3.1 – Econometric Liquidity Proxy

The traditional definition of liquidity relies on the likelihood that an investor can liquidate quickly and a significant number of financial instruments, near at the current market price or without bearing abnormal transaction costs. Liquidity is a multidimensional concept. The degree of liquidity of financial security is relevant in asset pricing studies, corporate finance and the analysis of the market efficiency. It is also fundamental to point out the factors that lead the illiquidity of security and monitor these determinants to preserve the market integrity.

In the asset pricing literature, researchers have considered whether liquidity is a priced risk factor (e.g., Amihud and Mendelson 1986; Brennan and Subrahmanyam 1996; Amihud 2002; Pastor and Stambaugh 2003). In corporate finance, researchers have found that liquidity is related to capital structure, mergers and acquisitions, and corporate governance (e.g., Lipson 2003; Lipson and Mortal 2009; Bharath 2009; Chung et al. 2010).

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In this study, we will analyze the liquidity concept under the aspect of the market efficiency. Researchers and Analysts have developed econometrics or statistics-based measures to capture one or more dimensions of a security’s liquidity. (i.e., limited dependent variable model (Lesmond, D. A. et al. Review of Financial Studies, 12(5), 1113– 1141, 1999) and autocovariance of price changes (Roll, R., Journal of Finance, 39, 1127– 1139, 1984).

Liquidity proxies are frequently used in empirical research in instances where liquidity measurement – whether is based on high-frequency or low-frequency data – can be very computationally intensive; in addition, the liquidity proxy is not always warranted (sometimes it can raise questions regarding the effectiveness). This topic is broadly addressed in equity market (e.g., Goyenko, Holden, and Trzcinka (2009), Hasbrouck (2009), and Corwin and Schultz (2012)); bond markets (e.g., Schestag, Schuster, and Uhrig-Homburg, 2015), currency markets (e.g., Mancini, Ranaldo, and Wrampelmeyer (2013) and Karnaukh, Ranaldo, and Soderlind, 2015)), and commodity markets (e.g., Marshall, Nguyen, and Visaltanachoti (2012)).

In these and many other papers, researchers have been developing a variety of liquidity measures to tackle this dynamic and evolving topic. In turn, the variety of available liquidity measures reflects the multidimensional aspect of liquidity.

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For simplicity purpose, the definition of liquidity can be categorized in four dimensions of liquidity: trading quantity, trading speed, price impact, and trading cost. Some extant measures focus on a single length of liquidity, while others encompass several dimensions.

For instance, the trading cost dimension includes the bid-ask spread measure in Amihud and Mendelson (1986), the estimator of the effective spread in Roll (1984), and the effective tick estimator in Goyenko et al. (2009). The turnover measure of Datar et al. (1998) captures the trading quantity dimension. The measures in Amihud (2002) and Pastor and Stambaugh (2003) are relevant to price impact. The number of zero trading volume days in Liu (2006) emphasizes trading speed. Lastly, and different from the others, the measure in Lesmond et al. (1999) encompasses several dimensions of liquidity.

Among the available measures, this Dissertation emphasis on various liquidity proxies, including the five that are commonly estimated using low-frequency data (i.e., daily closing prices) and three that are commonly estimated using high-frequency data (i.e., intraday trades and quotes).

The low-frequency measures are in Roll (1984), Amihud (2002), High-Low, BidAsk Spread, and Premium on NAV. The high-frequency measures are the percent effective spread, the percent quoted spread and the weighted quoted spread. The low-frequency proxies are valuable because they are more flexible to the study of liquidity over different exchanges and relatively long-time horizons. However, they are limited because they do not directly reflect actual trading processes, while the high-frequency measures do. Thus,

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high-frequency liquidity proxies are often used as benchmarks to determine the best lowfrequency proxy. This is not a general criterion, however, because each measure captures a different dimension of liquidity and may lead to different results in specific crosssectional or time-series applications.

3.2 – Previous Literature

Fixed Income ETFs are by construction characterized by a liquidity discrepancy that arises from changes in the market structure of exchange-traded funds and the underlying holdings. Unlike equity ETFs in which both the ETFs and the underlying instruments trade on stock exchanges, the underlying bond trades in a decentralized overthe-counter (OTC) market while a bond ETFs trade on an exchange platform.

The exchange market structure facilitates trade by centralizing the communication of bid and offer prices to all direct market participants; Over-the-counter markets rely on mutual networks of exchange counterpart centered around dealers. Moreover, in contrast to exchanges, dealers in an OTC market can pull out from market-making at any time, triggering the market liquidity to dry up and disrupting the trading ability of financial instruments.

The liquidity concern between bond ETFs and the underlying bonds has become noteworthy topic since the Financial Crisis, despite the deteriorating liquidity in the

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corporate bond market and the increase in financial regulation, the popularity of bond ETFs continued to skyrocket as financial assets.

The first corporate bond ETF was introduced in 2002; since then bond ETFs have grown to more than $800 billion in assets under management by 2017, up from only $3.9 billion in 2007 representing a growth rate of 20000% over ten years or on average nearly 70% of yearly AuM growth. In the last ten years, there was an increase in corporate bond issuance. Despite this growth, bond markets have become less liquid evidenced by the decline both in turnover by comparing the number of bonds outstanding to bond trading volumes and in broker-dealer inventories8. [Figure 7]

Financial intermediaries have become more constrained in their ability to inventory bonds on balance sheet due to more stringent capital requirement rules following Basel III and in their ability to trade bonds because of Dodd-Frank legislation on the warehousing and proprietary trading activities of broker-dealers.

Recent literature has highlighted potential liquidity risk implications of ETFs from market stability and contagion transmission perspective; In this section, we will review the academic literature about the Bond ETFs. In particular, the liquidity discrepancy in fixed income ETFs, giving that these risks are less explored and publicly debated, it is crucial to start questioning the drawbacks and the correct use of these instruments to be prepared and contain the consequences in the extreme event scenarios.

8

Source: New York Federal Reserve Bank and Bloomberg, as of March 31, 2017.

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One of the most fascinating aspects of liquidity risk is its measurement since it is not possible to directly observe it. Most measures of liquidity proxies have been developed are confounded by the fact that they mix liquidity risk with varying amounts of other risk factors.

For simplicity, we divided the previous literature into three subsections; each paragraph reflects at least one dimension of liquidity and how academics try to tackle the problem. Given bond ETFs are a hybrid financial instrument, researchers have consequently analyzed the issue under different perspective and method, but all of them have adjusted their approach9 to include the distinct characteristic of Bond and ETFs instruments.

3.2.1 – ETF Liquidity under Inventory Model

Calamia, Deville and Riva (2016) in their research “The provision of liquidity in ETF markets: the evidence from European markets”, they have developed an ETF inventory model that is taking in account for the illiquidity costs borne by a risk-averse Authorized Participants upon Creation and Redemption mechanism. The resulting implications are that ETF spreads are not just determined by the liquidity of underlying’s holdings.

9

Most of liquidity proxy are developed for equity instruments.

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The ETF spreads are also decreasing in ETF trading activity and increase in the underlying index volatility. Calamia et al. tested these implications on the European ETF markets. Consistent with their model, ETF spreads are affected by inventory-related variables (risk premium for holding ETF inventory, illiquidity parameter that reflects the costs of using the Creation/Redemption process, etc..), while the underlying basket spread has no significant impact but for low trading volume ETFs.

During the market distress, a sudden deterioration in market liquidity may affect some ETFs than other types of the asset. In 2014 Carolyn A. Wilkins, a Senior deputy of the Bank of Canada governor and member of the G20 Financial Stability Board suggested that there could be a reverse relationship: in other words, ETF liquidity could negatively impact market liquidity during times of stress. It may be not a one-way relationship. [Figure 8]

In their research paper, Calamia, Deville and Riva set out to test these presumed relationships. They want to identify the actual determinants of an ETF’s liquidity, which are mostly unknown. For the average ETF, it turns out; basket liquidity has a surprisingly small effect on ETF liquidity. The authors find out that the spread of the underlying basket explains only 7% of the volatility of ETF spreads.

Calamia, Deville and Riva introduce an inventory model of market making that reflects the actual costs faced by ETF traders. The model includes many expansions to existing approaches:

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1)

Introduction of a parameter that echoes the costs of using the creation/redemption mechanism at the end of each trading day.

2)

A risk premium for holding ETF inventory;

3)

AP (or market maker) can manage inventory in the ETF secondary market;

The empirical outcomes shown by Calamia, Deville and Riva confirm their theoretical model. The average bid-ask spread of the underlying security is a marginal factor in determining ETF liquidity. Several inventory-related factors play a key role in determining ETF bid-offer spreads instead: the trading activity in the ETF, the volatility of the underlying index, the ETF’s size, the availability of suitable inventory hedges (such as index futures, options) and the funding costs faced by the market maker. For the most highly traded ETFs, liquidity begets liquidity: a large part of such ETFs’ liquidity derives from specific characteristics intrinsic to these funds. For less traded ETFs, the liquidity of the underlying basket plays a more critical role. [Figure 9]

Pan and Zeng (2017) propose a complementary arbitrage effect in the inventory model framework: since APs have a dual role in financial markets they may sporadically consume more liquidity than they provide. In fact, in their research, they focus on the corporate bond ETFs and examine the overlapping role of APs, the conflict of interests start to arise when economic incentive as dealers in the underlying bond market (OCT) collide with the arbitrageur function in the primary market.

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In their empirical analysis, they show that there is a liquidity mismatch that could reduce the market efficiency and increase the volatility of these ETFs. This may happen when there is selling pressure by investors during times of market stress. APs may not be willing to engage in arbitrage when the underlying securities are illiquid. Because corporate bonds that are not liquid and cannot be immediately executed without incurring large price impacts and high costs of trade, in the same circumstance, APs may withdraw from the market and hold bond inventory.

Because APs are not legally bound by contract obligations to perform ETF arbitrage, APs may occasionally become liquidity seekers and withdraw from ETF arbitrage rather than acting providers in the ETF market. The two effects arise contemporaneity are arbitrage mechanism and fixed income inventory management.

When this conflict is small, liquidity mismatch reduces the arbitrage capacity of ETFs; as the conflict increases, an inventory management motive arises that may even distort ETF arbitrage, leading to substantial relative mispricing.

The authors present evidence that APs’ trading volume declines when market volatility (captured by the VIX) is high, suggesting that APs operate like arbitrageurs who have limited capital, and thus withdraw from the market when volatility is high. In their paper, the authors model the assumptions above and display new evidence that ETF arbitrage is subject to various frictions in both ETF market and the underlying bond market. These frictions can reduce the smoothness of ETF arbitrage process, resulting in persistent

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relative small mispricing and potential market fragility. They show empirically how bond inventory positions and liquidity mismatch interact to lower APs' willingness to engage arbitrage opportunities, and present evidence on the impact of realized AP arbitrage on corporate bond returns and liquidity.

3.2.2 – Liquidity between Price Discovery and Crowding-Out effect

Investors regularly use ETFs as a low-cost instrument for directional bets on the market short-term market drift. As such, it is likely that ETF prices reflect news before it is incorporated into the prices of the underlying securities. Relatedly, ETFs add layer of liquidity to the underlying securities through the arbitrage mechanism. By trading the ETF, investors embody index-related information indirectly into the price of the ETF holdings. In turn, APs ensure that the prices of the underlying securities do not diverge from those of the ETF. The result is that this arbitrage activity helps transmit the systematic information from the ETF to the underlying securities and provides liquidity to the underlying securities. Thus, ETFs could potentially improve price discovery at the index level and enhance liquidity at the level of the underlying basket.

Madhavan and Sobzyk (2016) advance the view that ETFs enhance the efficiency of financial markets. The study argues that because ETFs provide a cost-effective tool for investors who try to take directional bets on the index, they will impound the news before the underlying securities. According to the authors, if the arbitrage activity is frictionless, ETFs do not only propagate shocks into securities but instead, stimulate price discovery 48


mechanism. In other words, the price discovery mechanism between at the ETF level and the underlying securities level can propagate in both ways.

Glosten, Nallareddy, and Zou (2016) find that security incorporates information quicker once they are designated to be part of ETF portfolios. They argue that some of the increased comovement that was documented by other researchers can be explained by better incorporation of systematic information into stock prices.

This evidence is consistent with Da and Shive (2016) study that documents an increased comovement in returns in the financial instruments that are part of an index. When investors trade on the news related to the index, they trade the ETF more actively. The mechanical basket trading of the underlying securities tied to the ETF through arbitrage exhibits in higher return comovement and causes basket security to lose part of their idiosyncratic volatility. Therefore, the individual security response is expected to be less sensitive and less timely to the idiosyncratic risks.

Not all researchers agree that ETFs improve the informational efficiency of the securities in their baskets. Israeli, Lee, and Sridharan (2015) show that securities that are owned by ETFs have higher trading costs (bid-ask spreads and market liquidity) and higher comovement with the index which means higher return synchronicity. ETF ownership can also lead to a lower informational efficiency, measured as lower future earnings response coefficients and receive less analyst coverage.

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Da, Engelberg, and Gao (2015) present evidence showing that retail investor sentiment is associated with the ETF volatility. They find that household sentiment, measured by the volume of search engine queries (e.g., "recession," "unemployment," and "bankruptcy"), predicts the price volatility of the largest ETFs. Broman (2016) documents that the degree and direction of mispricing between ETFs and their underlying securities comove excessively across ETFs. He concludes that these findings are consistent with the idea that the high liquidity of ETFs attracts a clientele of short-horizon noise traders with correlated demand across investment styles.

Further Empirical studies confirm that ETFs have multiple effects in opposite directions on the liquidity of the underlying securities. In one direction, as argued above, ownership by ETFs can increase liquidity in the underlying securities. This happens due to the arbitrage trades that take place between the ETF and the underlying securities. Marshall, Nguyen, and Visaltanachoti (2015) document patterns that illustrate the activity of arbitrageurs. They find that the liquidity of ETFs is correlated with the liquidity of the underlying stocks. The more liquid the underlying stocks are the higher ability of arbitrageurs to engage in arbitrage trades, making the ETF liquid as well. However, ETFs have lower asymmetric information, more algorithmic trading, and an active primary market. Using a wide-ranging database of over 600 ETFs, they find that despite the differences between ETF and basket liquidity, proxies such as Daily Spread, High-Low, Amihud, and Roll are all a good proxy in capturing changes in effective and quoted spread transaction costs.

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Agarwal, Hanouna, Moussawi, and Stahel (2016) document that the liquidity of ETFs comoves with the liquidity of the assets in the ETF baskets. The authors show that higher ETF ownership is associated with higher comovement of liquidity among large and small stocks alike, and they document that such liquidity commonality has increased in recent years and that it is greater during crisis versus non-crisis periods. In the opposite direction, some researchers support that ETFs can decrease the liquidity of the underlying securities. Since ETFs provide an inexpensive way to trade, they can crowd out traders from the underlying assets and detract liquidity. Petajisto (2016) reports a significant deviation of ETF prices from those of the underlying assets, on the average inside within a band of about 200 basis points, particularly for illiquid assets. The deviations are greater in funds holding international or illiquid securities where net asset values are most difficult to determine in real time. Active trading strategies exploiting such inefficiencies produce substantial abnormal returns before transaction costs, providing further proof of short-term mean-reversion in ETF prices.

Piccotti (2014) documents that in some ETFs, the deviation from the value of the underlying assets is permanent, and he argues that market segmentation may explain this tendency for ETFs to trade at a premium to NAV as well as the life-cycle pattern in premiums. Investors may be willing to recompense the access to assets with higher liquidity with a premium, especially for those foreign securities and fixed income ETFs that are considered the most valuable to retail investors.

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Dannhauser (2016) finds that the introduction of corporate bond ETFs leads to a decrease in the liquidity of the underlying bonds, suggesting a crowding out effect. In particular, the author finds that financial innovation has a significant and long-term positive valuation impact on the underlying securities. Other evidence is that higher ETF ownership lowers bond yields but has an insignificant or negative impact on bond liquidity: the reason of this is due to migration of liquidity traders from the underlying OTC market to ETF market rather than ETF arbitrage by APs.

3.2.3 – Propagation of ETF shocks to Underlying Securities

The additional layer of liquidity that ETFs provide may have undesired effects at the level of the underlying securities. Several studies present evidence consistent with the idea that ETFs may inject indirectly non-fundamental volatility at the level of the underlying basket. Prior empirical research in other asset classes presents a mixed picture about whether trading activity in derivatives or mutual funds affects the prices of the underlying securities. The Mechanism through which ETFs may transmit noise into the underlying securities is explored in different studies.

Schultz (2001) provides systematic evidence regarding the liquidity mismatch between equity trading on exchanges and OTC corporate bond trading. He documents that even institutional trades in corporate bonds incurred much larger transactions costs than those in equity markets. Since bonds are traded OTC, it is harder for an arbitrageur to establish or unwind its arbitrage positions when the underlying bonds become less liquid 52


due to increasing search costs or information asymmetry which are worsened by the opaque and bilateral nature of OTC markets. Moreover, less liquid bonds are usually also riskier and associated with higher short costs, potentially generating even greater limits to arbitrage. The differences in liquidity because of market structure gives rise to some specific limits to arbitrage in the corporate bond ETF setting.

Malamud (2015) in his paper “A Dynamic Equilibrium Model of ETFs� develops a model for ETFs that accounts the two-layers market structure: the centralized exchange (secondary market) and a creation/redemption mechanism (primary market) operating through APs.

For simplification purpose, in the Malamud’s work, the market is populated by four classes of participants: ETF sponsors that provide liquidity to APs in the primary market; Securities dealers; APs that supply liquidity in the ETF market and demand liquidity in the basic securities market and ETFs investors. In this model (as in the real world), APs perform a dual role of being both market makers and arbitrageurs. When income shocks are realized, APs use the creation/redemption mechanism to adjust their positions and load some of their inventory onto the ETF sponsor. The creation/redemption mechanism, combined with the arbitrage activity of APs, may make the markets unstable and lead to momentum in asset returns and a persistent ETF pricing gap.

The Author shows that the creation/redemption mechanism propagates temporary liquidity shocks into the underlying securities and the temporary demand shocks may have

53


long-lasting impacts on future prices. When in the primary market, the bid-ask spread continues to shrink due to the competition among fund issuers, this leads to an improvement in the structural liquidity, letting the Creation and Redemption mechanism more accessible and therefore enchasing the volatility propagation. As a result, a frictionless primary market may strengthen the volatility of both ETF price and the underlying assets. However, introducing new ETFs may reduce both the volatility and comovement in the returns and may improve the liquidity of the underlying basket.

Ben-David, Franzoni, and Moussawi (2013,2016) investigate a similar mechanism. A demand shock can move the ETF price from the fundamental value. If there is limited liquidity (as happen is the fixed income segment) in the underlying securities’ market, the underlying securities’ prices are temporarily pushed away from the fundamental value. In the long run, liquidity flows back into the market, and both the ETF price and the underlying securities’ prices revert to their fundamental value. The liquidity shocks can propagate to the underlying securities through the arbitrage channel, and ETFs may increase the non-fundamental volatility of the securities in their baskets. In their research, they exploit exogenous changes in index membership and find that security with higher ETF ownership display significantly higher volatility. ETF ownership increases the negative autocorrelation in asset’s prices.

54


Chapter IV. Sample

In this section describes the current ETFs landscape, the following subsection reveal the source of our data sample and why we use specific criteria to filter the data. Given most of the data on AP’s inventory are not publicly available, we tried to combine information from a different data provider to have the best estimate of liquidity proxy.

4.1- ETFs Landscape

In the past ten years, exchange-traded funds (ETFs) have seen an outstanding activity and growth. Regulation, product innovation, and investor appetite have driven its global Asset under management (AuM) to record levels. ETF is often promoted as better and cheaper mean of investment vehicle than mutual funds or the active investing (Stock picking or Mean-Variance portfolio). Stock prices have become more correlated, and less close the fundamental value of the company. Moreover, the company share is more linked to the business “factors” such as its fund membership, ETF inclusion or quantitative-factor attributes10.

The first ETF was launched in January 1993, The American Stock Exchange (AMEX) released the S&P500 Depositary Receipt (called Spider or SPDR). It was very

“Directors’ Dilemma: responding to the rise of passive investing” by Global Markets Institute on January 2017 10

55


popular and today is still one of the most traded ETFs on the exchange market. Although the first ETF was introduced in 1993, it took more than 15 years to see this asset class to reach the size and the trust of retail and institutional investors. [Figure 10]

The idea of index investing did not just come with the introduction of ETFs of 25 years ago. In response to academic research suggesting the advantages of passive investing, Wells Fargo11 launched index mutual funds in 1969 for pension funds, the execution was “a nightmare�, because the strategy was based on an equal-weighted index of NYEX equities. John Bogle (founder of The Vanguard Group) would follow a couple of years later, launching the first public index mutual fund on Dec. 31, 1975 to the public and focusing on proving low fee structure and reasonable returns. The fund was called Vanguard 500 Index Fund (VFINX) and it tracked the S&P 500 and started with just $11 million in assets. Referred to derisively by some as "Bogle's folly12" the AUM of this fund crossed $390 billion on Dec. 31 2017.

Once it was clear that the investors have an appetite for passive investment, other competitors started to squeeze into the market. Barclays entered the business in 1996, State Street in 1998 and Vanguard began offering ETFs in 2001. As of the end of 2011, there were over 15 issuers of ETFs13, but currently the number has increased to 94. [Table 4]

11

John A. McQuown and William L. Fouse Some of the early critics was on the mediocrity of the investing method since there was no intention to identify the best-performing stocks or undervalued firm. 13 A Brief History Of Exchange-traded Funds - Yahoo.com 12

56


4.2 - Data Selection

As 31/08/2017, 7727 Exchange-Traded Products were publicly available for investors. The AuM was approximately $4.798 Billion14. Equity ETFs were the most popular segment since it accounted 80% of total ETP. Instead, Bond ETFs had only 18% of the market shares, but it had for the past ten years an annual average growth of 26%. Currently, Bond ETFs has a total of $833 Billion of AuM; ETF distribution market it is concentrated mainly in US (70%) and Europe (15%). [Figure 11]

We Collected the data of all bond ETF available from Bloomberg, Morningstar, DataStream, ETF.com, Bond ETF issuer websites and Central Banks data archive. Our Data Sample is composed by daily data from January 2011 to December 2017, but our sample of study will start from February 2012 because for some specific liquidity measures we needed at least one-year historical data to elaborate the estimation.

From 1278 Bond ETPs, we selected only Funds that had Exchange-Traded Funds and Open-End Investment Company structure; then we excluded the leverage products and those who had inception date after 2011. Among the remaining, we filtered for funds who had AuM less than $2 Billion. We end up with 57 Bond ETFs.

Instead of taking the first 20 major Bond ETFs regarding AuM, we preferred to divide the sample into eight categories and then take the most representative for each segment. In this

14

According Bloomberg and ETFGI LLP

57


way, we formed a pool of 16 Bond ETFs that can be analyzed both in the term as a sample of the whole market and to make a comparison between European and U.S. Bond ETFs market. [Table 5]

Our Sample data has $ 282,822 Million of AuM, which represent 34% of the total Bond ETF market. 14 of them are issued by BlackRock, and both State Street and Vanguard has only one ETF in our sample. We included 3 European ETF to have a more homogenous representation of the industry, and it is also more congruent with the current market distribution [figure 12]. In the [Table 6] we provided additional information about the sample Bond ETFs: We break down each security into a subsection of credit quality, investment sector and average basket maturity. Even if the ETF manager continues to adjust their portfolio to track the index, that characteristic should constant in the long term. In addition to the low-frequency data, we also retrieved intraday data of our sample form Bloomberg; given the time frame limitation of the data provider, we could gather only recent months data. Since our research question is about the liquidity of Bond ETFs instruments during market distress situations, we looked at the peak of the last three months of VIX index15 (as a proxy for market risk) and Federal funds target rate16 (as a proxy for interest rate risk) to select more volatile trading days.

The dates are respectively

15/11/2017 and 14/12/2017. To complete the analysis, we also added a “normal� date (13/12/2014) to make more consistent the high-frequency data proxy. [Figure 14}

15

The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the stock market's expectation of volatility implied by S&P 500 index options. 16 The fed funds rate is the interest rate at which depository institutions (banks and credit unions) lend reserve balances to other depository institutions overnight, on an uncollateralized basis.

58


Chapter V. Methodology

In this chapter, we described both low and high-frequency liquidity measures. Then we used the high-frequency liquidity proxies as a benchmark to select the best lowfrequency proxy. Ultimately, after developed our inventory risk model, we performed a regression analysis to test our research question.

5.1 – Low-Frequency Liquidity Proxies

Roll (1984) develops a measure of the effective bid-ask spread. He assumes that the true value of a stock follows a random walk and that Pt, the observed closing price on day t, is equal to the stock’s true value plus or minus half of the effective spread. He also assumes that a security trades at either the bid price or the ask price, with equal frequency. This relationship can be expressed as follows:

đ?‘ƒđ?‘Ą = đ?‘ƒđ?‘Ąâˆ— + đ?‘„đ?‘Ą đ?‘„đ?‘Ą đ??źđ??źđ??ˇ ~ + 1 đ?‘¤đ?‘–đ?‘Ąâ„Ž đ?‘?đ?‘&#x;đ?‘œđ?‘?

đ?‘ 2

(6)

1 1 ; − 1 đ?‘¤đ?‘–đ?‘Ąâ„Ž đ?‘?đ?‘&#x;đ?‘œđ?‘? 2 2

Qt is a dummy variable that indicates the transaction at time t (“+1� buyer initiated or ‘-1� seller initiated). This model assumes that the expected value of Qt is zero and there are no changes in the fundamental value of the asset. Thus, the price changes follow the following process:

59


đ?‘

đ?‘

2

2

∆đ?‘ƒđ?‘Ą = ∆đ?‘ƒđ?‘Ąâˆ— + (đ?‘„đ?‘Ą − đ?‘„(đ?‘Ąâˆ’1) ) = (đ?‘„đ?‘Ą − đ?‘„(đ?‘Ąâˆ’1) )

(7)

Under the assumption that Qt is IID, the variance and the covariance of ∆đ?‘ƒđ?‘Ą are: đ?‘‰đ?‘Žđ?‘&#x; [∆đ?‘ƒđ?‘Ą ] =

đ?‘ 2 2

đ??śđ?‘œđ?‘Ł [∆đ?‘ƒđ?‘Ą , ∆đ?‘ƒđ?‘Ąâˆ’1 ] = đ??śđ?‘œđ?‘Ł [(đ?‘„đ?‘Ą − đ?‘„đ?‘Ąâˆ’1 )

(8) đ?‘ đ?‘  , (đ?‘„đ?‘Ąâˆ’1 − đ?‘„đ?‘Ąâˆ’2 ) ] 2 2

(9)

đ?‘ 2 [đ?‘?đ?‘œđ?‘Ł(đ?‘„đ?‘Ą − đ?‘„đ?‘Ąâˆ’1 ) , đ?‘?đ?‘œđ?‘Ł(đ?‘„đ?‘Ąâˆ’1 − đ?‘„đ?‘Ąâˆ’2 ) ] = 4 đ?‘ 2 [−đ?‘Łđ?‘Žđ?‘&#x;(đ?‘„đ?‘Ąâˆ’1 ) ] = 4 đ?‘ 2 1 1 đ?‘ 2 2 2 (1 (−1 = [ − 0) + − 0) ] = − 4 2 2 4

đ??śđ?‘œđ?‘Ł [∆đ?‘ƒđ?‘Ą , ∆đ?‘ƒđ?‘˜âˆ’1 ] = 0 , đ?‘“đ?‘œđ?‘&#x; đ?‘˜ > 1

(10)

Solving for S yields the Roll’s Effective Spread estimator:

đ?‘† = 2 √−đ??śđ?‘œđ?‘Ł (∆đ?‘ƒđ?‘Ą , ∆đ?‘ƒđ?‘Ąâˆ’1 )

(11)

If P* is fixed so that the prices take only two values, bid or ask, if the current price is ask, then the change between current and previous price must be either 0 or -s and the price change between current price and next price must be either 0 or s. Analogous possible changes apply when the current price is the bid.

60


Using daily data on price changes, the autocovariance that defines S can be positive, rather than negative, so S is undefined. In this case, researchers suggest treating the Roll estimate to zero (S= 0) or to multiply the covariance by negative one and then after the calculation (S= -1), multiply the estimate to obtain a negative spread estimate.

The Amihud (2002) illiquidity measure is a representative proxy for price impact, i.e., the daily price response associated with one dollar of trading volume.

đ?‘‘đ?‘–,đ?‘Ą

đ??´ đ?‘–,đ?‘Ą = ∑ đ?‘—=1

đ?‘‘đ?‘–,đ?‘Ą

|đ?‘&#x; đ?‘–,đ?‘— | ∗ $đ?‘Łđ?‘œđ?‘™ đ?‘–,đ?‘—

(12)

|đ?‘&#x; đ?‘–,đ?‘— | is daily absolute return; $đ?‘Łđ?‘œđ?‘™ đ?‘–,đ?‘— is the daily dollar volume; đ?‘‘đ?‘–,đ?‘Ą is the number of days for which the data is available for the security i in t

In our analysis, we assume both t = d = 1 (daily illiquidity) and t =252 (yearly illiquidity). A higher đ??´ đ?‘–,đ?‘Ą implies that the security is more illiquid, the bond ETF’s price is more volatile in response to a small change in the dollar volume.

61


5.2 – High-Frequency Liquidity Proxies

The percentage quoted spread is the immediate difference between the ask quotation (the price at which share can be purchased) and bid quotation (price at which share can be sold), it can be seen as the cost of a round-trip transaction and is generally expressed as a proportion of the average of the bid and ask prices:

% đ?‘žđ?‘˘đ?‘œđ?‘Ąđ?‘’đ?‘‘ đ?‘ đ?‘?đ?‘&#x;đ?‘’đ?‘Žđ?‘‘ đ?‘–,đ?‘Ą =

đ??´đ?‘ đ?‘˜đ?‘–đ?‘Ą − đ??ľđ?‘–đ?‘‘đ?‘–đ?‘Ą đ?‘€đ?‘–đ?‘Ą

(13)

The Percentage Weighted spread is a liquidity measure that takes into account the $ volume of each concluded transaction in each subsample and the related spread.

đ?‘›

% đ?‘¤đ?‘’đ?‘–đ?‘”â„Žđ?‘’đ?‘‘ đ?‘ đ?‘?đ?‘&#x;đ?‘’đ?‘Žđ?‘‘ đ?‘–,đ?‘Ą = ∑ đ?‘Ą =1

đ?‘Šđ?‘–,đ?‘Ą (đ??´đ?‘ đ?‘˜đ?‘–đ?‘Ą − đ??ľđ?‘–đ?‘‘đ?‘–đ?‘Ą ) đ?‘Šđ?‘Ąđ?‘œđ?‘Ą

(14)

The Percentage effective spread accounts for some trades that occur within the range of bid-ask spread. In fact, the latter liquidity measure may overestimate the realized amount of the transaction cost. To adjust this bias, for a given security the effective spread is computed as:

% đ?‘’đ?‘“đ?‘“đ?‘’đ?‘?đ?‘Ąđ?‘–đ?‘Łđ?‘’ đ?‘ đ?‘?đ?‘&#x;đ?‘’đ?‘Žđ?‘‘ đ?‘–,đ?‘Ą

đ?&#x2018;&#x2013;đ?&#x2018;&#x201C; đ?&#x2018;&#x192;đ?&#x2018;Ą+1 > đ?&#x2018;&#x192;đ?&#x2018;Ą â&#x2020;&#x2019; = { đ?&#x2018;&#x2013;đ?&#x2018;&#x201C; đ?&#x2018;&#x192;đ?&#x2018;Ą+1 < đ?&#x2018;&#x192;đ?&#x2018;Ą â&#x2020;&#x2019;

62

Ě&#x2026;Ě&#x2026;Ě&#x2026;Ě&#x2026;Ě&#x2026; đ??´đ?&#x2018; đ?&#x2018;&#x2DC;đ?&#x2018;&#x2013;,đ?&#x2018;Ą â&#x2C6;&#x2019; đ?&#x2018;&#x192;Ě&#x2026;đ?&#x2018;&#x2013;,đ?&#x2018;Ą Ě&#x2026;Ě&#x2026;Ě&#x2026;Ě&#x2026;Ě&#x2026; đ??ľđ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2013;,đ?&#x2018;Ą â&#x2C6;&#x2019; đ?&#x2018;&#x192;Ě&#x2026;đ?&#x2018;&#x2013;,đ?&#x2018;Ą

(15)


Following the convention, for each ETF I collected the intraday data, then I divided them into three groups: â&#x20AC;&#x153;tradeâ&#x20AC;?, â&#x20AC;&#x153;best bidâ&#x20AC;? and â&#x20AC;&#x153;best askâ&#x20AC;? and then combined them into 1-minute time span.

5.3 - Model Setup

By using the inventory risk model developed by Stoll (1978) and then extended by Calamia et al. (2016) for their study in equity ETF. We developed a simple model that takes into account for the underlying bond securities characteristics and the EFT mechanisms. The model has three dates. [Figure 13] Time is index by t = 0, 1, 2. The secondary market is represented by n+1 securities (where securities i = 1, â&#x20AC;Ś, n are bonds and security n +1 is the bond ETF).

Trades on the secondary market can take on date t = 0 and t =1. At each time the market maker faces an order of size đ?&#x2018;Ľđ?&#x2018;Ą from market participants, that the AP clears at price đ?&#x2018;?đ?&#x2018;Ą by trading đ?&#x2018;&#x17E;đ?&#x2018;Ą ETF units. If đ?&#x2018;&#x17E;đ?&#x2018;Ą > 0 (đ?&#x2018;&#x17E;đ?&#x2018;Ą < 0), it indicates a buy (sale) from the AP since the inventory is not sufficient (is overload). At t = 2, AP holds đ?&#x2018;&#x201E;2 = đ?&#x2018;&#x17E;0 + đ?&#x2018;&#x17E;1 , if needed, she will initiate the creation/redemption mechanism to unwind their positions back to the initial equilibrium. (đ?&#x2018;&#x201E;0 = 0) The fundamental value of the individual bond đ??ľđ?&#x2018;&#x2013;,đ?&#x2018;Ą of the bond i follow the below process:

63


t

đ?&#x2018;˘đ?&#x2018;&#x2013;,đ?&#x2018;Ą = đ?&#x2018;˘Ě&#x2026;đ?&#x2018;&#x2013; + â&#x2C6;&#x2018;ɤ=1 É&#x203A;i,ɤ

with t =1,2

(16)

Where É&#x203A;đ?&#x2018;&#x2013;,ɤ is the innovation in bond i fundamental value, which is normally distributed with mean zero. We also make the assumptions: i)17 đ?&#x2018;?đ?&#x2018;&#x153;đ?&#x2018;Ł(É&#x203A;đ?&#x2018;&#x2013;,ɤ , É&#x203A;đ?&#x2018;&#x2013;,ɤ+đ?&#x2018;&#x2DC; ) = 0 for â&#x2C6;&#x20AC;đ?&#x2018;&#x2DC; â&#x2030; 0 and ii)18 đ?&#x2018;?đ?&#x2018;&#x153;đ?&#x2018;Ł(É&#x203A;đ?&#x2018;&#x2013;,ɤ , É&#x203A;đ?&#x2018;&#x2014;,ɤ ) = đ?&#x153;&#x17D;đ?&#x2018;&#x2013;,đ?&#x2018;&#x2014; for â&#x2C6;&#x20AC;(đ?&#x2018;&#x2013;, đ?&#x2018;&#x2014;). On t = 2, the AP must liquidate the inventory đ?&#x2018;&#x201E;2 she accumulated on the ETF. This lead trading đ?&#x2018;&#x201E;2 units of underlying basket on the secondary market. We assume that bonds are non-perfectly liquid, and we denote Ő&#x201C;đ?&#x2018;&#x2013; the illiquidity parameter of the bond i. As the AP unwind their Q position, the cash flow19 associated with the transaction is equal to đ?&#x2018;&#x203A;

đ??śđ??š = đ?&#x2018;&#x201E; â&#x2C6;&#x2018;(đ?&#x2018;˘đ?&#x2018;&#x2013;,2 â&#x2C6;&#x2019; Ő&#x201C;đ?&#x2018;&#x2013; đ?&#x2018;&#x201E;)

(17)

đ?&#x2018;&#x2013;=1

In other words, đ?&#x2018;&#x201E;2 â&#x2C6;&#x2018;đ?&#x2018;&#x203A;đ?&#x2018;&#x2013;=1 Ő&#x201C;đ?&#x2018;&#x2013; is the illiquidity cost borne by the AP. By denoting c as the initial cash, the AP Wealth at t=2 is equal to:

đ?&#x2018;&#x203A;

đ?&#x2018;&#x160;2 = đ?&#x2018;? â&#x2C6;&#x2019; đ?&#x2018;&#x17E;0 đ?&#x2018;&#x192;0 â&#x2C6;&#x2019; đ?&#x2018;&#x17E;1 đ?&#x2018;&#x192;1 + (đ?&#x2018;&#x17E;0 + đ?&#x2018;&#x17E;1 ) â&#x2C6;&#x2018;[đ?&#x2018;˘đ?&#x2018;&#x2013;,2 â&#x2C6;&#x2019; Ő&#x201C;đ?&#x2018;&#x2013; (đ?&#x2018;&#x17E;0 + đ?&#x2018;&#x17E;1 )]

(18)

đ?&#x2018;&#x2013;=1

We assume that the AP has a CARA20 utility function with an absolute risk equal to A The optimization expression can be written as:

17

Assumption derived from market efficiency theorem Aim at capturing the cross-sectional dependence of price innovation among underlying bond basket 19 On a perfect liquid market, the CF would be equal to the sum of ui,2 20 Constant absolute risk aversion 18

64


max E(â&#x2C6;&#x2019;exp â&#x2C6;&#x2019; AW2} )

(19)

q0 q1

Eq. (16) is solved by backward induction. On t = 1, the optimal quantity traded by AP is:

max đ?&#x2018;? â&#x2C6;&#x2019; đ?&#x2018;&#x17E;0 đ?&#x2018;&#x192;0 â&#x2C6;&#x2019; đ?&#x2018;&#x17E;1 đ?&#x2018;&#x192;1 + (đ?&#x2018;&#x17E;0 + đ?&#x2018;&#x17E;1 )đ??¸(đ?&#x2018;˘đ??ľ,2 ) â&#x2C6;&#x2019; (đ?&#x2018;&#x17E;0 + đ?&#x2018;&#x17E;1 2 ) [Ő&#x201C;đ??ľ + q1

đ??´ 2 đ?&#x153;&#x17D; ] 2 đ??ľ

(20)

Where đ??¸(đ?&#x2018;˘đ??ľ,2 ) = â&#x2C6;&#x2018;đ?&#x2018;&#x203A;đ?&#x2018;&#x2013;=1 đ?&#x2018;˘đ?&#x2018;&#x2013;,2 ; Ő&#x201C;đ??ľ = â&#x2C6;&#x2018;đ?&#x2018;&#x203A;đ?&#x2018;&#x2013;=1 Ő&#x201C;đ?&#x2018;&#x2013; and đ?&#x153;&#x17D;đ??ľ2 = 1â&#x20AC;˛ đ?&#x203A;š1 it measures the variance of the price of the underlying basket.

The first order condition yields: đ?&#x2018;&#x17E;1â&#x2C6;&#x2014; =

đ??¸1 (đ?&#x2018;˘đ??ľ,2 ) â&#x2C6;&#x2019; đ?&#x2018;&#x192;1 đ?&#x203A;Ź1

(21)

Where đ?&#x203A;Ź1 = 2Ő&#x201C;đ??ľ + đ??´đ?&#x153;&#x17D;đ??ľ2 and đ??¸1 (. ) is the expectation conditional on the information available on t = 1.

At time t =1, the order flow đ?&#x2018;Ľ1 is price sensitive and takes the form:

đ?&#x2018;Ľ1 = đ?&#x203A;˝[ đ??¸1 (đ?&#x2018;˘đ??ľ,2 ) â&#x2C6;&#x2019; đ?&#x2018;&#x192;1 ]

(22)

Where đ?&#x203A;˝ > 0 captures the APâ&#x20AC;&#x2122;s incentive for the traded ETF. The higher is đ?&#x203A;˝, the more APs are willing to exploit the price discrepancy between ETF price and its underlying

65


basket. it is important to note that a high đ?&#x203A;˝ can induce the AP to accept the demand that moves from her optimal inventory level value even if the prices discrepancy is small. On the contrary, a low đ?&#x203A;˝ can hold the AP to enter the market as counterparty even if the arbitrage opportunity (đ??¸1 (đ?&#x2018;˘đ??ľ,2 ) â&#x2030;Ť đ?&#x2018;&#x192;1 ) ) is large

Setting đ?&#x2018;&#x17E;1â&#x2C6;&#x2014; = â&#x2C6;&#x2019; đ?&#x2018;Ľ1 , the equilibrium price and corresponding quantity at t =1 are:

đ?&#x2018;&#x192;1â&#x2C6;&#x2014; = đ??¸1 (đ?&#x2018;˘đ??ľ,2 ) â&#x2C6;&#x2019;

đ?&#x2018;&#x17E;1â&#x2C6;&#x2014; = â&#x2C6;&#x2019;

đ?&#x203A;Ź1 đ?&#x2018;&#x17E; 1 + đ?&#x203A;˝đ?&#x203A;Ź1 0

đ?&#x203A;˝đ?&#x203A;Ź1 đ?&#x2018;&#x17E; 1 + đ?&#x203A;˝đ?&#x203A;Ź1 0

(23)

(24)

The equilibrium ETF price at t = 0, can be found by replacing đ?&#x2018;&#x192;1 [ Eq. 23] and đ?&#x2018;&#x17E;1 [Eq. 24] in the AP terminal wealth on t =2 [Eq. (18)], and differentiating with respect to đ?&#x2018;&#x17E;0 to solve the maximization function [Eq. 19]

đ?&#x2018;&#x203A;

đ?&#x2018;&#x192;0 = đ?&#x2018;˘Ě&#x2026;đ??ľ â&#x2C6;&#x2019; đ?&#x2018;&#x17E;0 đ?&#x203A;Ź0 = â&#x2C6;&#x2018; đ?&#x2018;˘Ě&#x2026;đ?&#x2018;&#x2013; â&#x2C6;&#x2019;

đ?&#x2018;&#x17E;0 [đ??´đ?&#x153;&#x17D;đ??ľ2

đ?&#x2018;&#x2013; =1

(1 + 4đ?&#x203A;˝Ő&#x201C;đ??ľ )(2Ő&#x201C;đ??ľ + đ??´đ?&#x153;&#x17D;đ??ľ2 ) + ] [1 + đ?&#x203A;˝(2Ő&#x201C;đ??ľ + đ??´đ?&#x153;&#x17D;đ??ľ2 )]2

(25)

[Eq. 25]21 states that the quoted spread per traded unit is the sum of two parts. The first component is the uncertainty the AP faces on the price at which she trades at time t +1 to offset the trade and it is related to the volatility of the underlying asset. The second term captures the effect of đ?&#x203A;˝ on the AP incentive to enter the marker to perform arbitrage 21

The full derivation of the model is illustrated in the appendix â&#x20AC;&#x201C; â&#x20AC;&#x153;model proofâ&#x20AC;?

66


mechanism. Assuming that the AP post quotes for the same quantity at the bid and the ask, the size of the ETF spread on t = 0 is given by:

đ?&#x2018;&#x2020;0 = 2 đ?&#x203A;Ź1 |đ?&#x2018;&#x17E;0 |

(26)

Since đ?&#x153;&#x17D;đ??ľ2 measures the variance of the price of the underlying bond basket. ETFs which replicate riskier indices should trade with higher spreads. Sector (included Bond) ETFs should thus exhibit higher spreads since the diversification based on a particular industry or segment is likely to be riskier since a part of idiosyncratic risk is not diversified away.

In Addition, from this model, we can derive an inverse relationship between ETF spread and AP incentive to enter creation and redemption process (đ?&#x203A;˝). On t =1, the AP faces a tradeoff between the price discrepancy (arbitrage opportunity) and larger inventory risk (higher liquidation costs). In the extremis case, when đ?&#x203A;˝ â&#x2020;&#x2019; +â&#x2C6;&#x17E; , the ETF spread at t = 0 is independent from the liquidity of the bond basket since the AP can fully offset her inventory on t = 1. Whereas when đ?&#x203A;˝ = 0 , no trade occurs at time t =1 and đ?&#x203A;Ź0 = 2(Ő&#x201C;đ??ľ + đ??´đ?&#x153;&#x17D;đ??ľ2 ) , this yields a direct transfer of illiquidity cost of basket of bonds to the ETF spread. In the general case, when đ?&#x203A;˝ is low the AP, despite her risk preferences, will move primary to adjust her own inventory of the underlying assets.

The Regression analysis objective is to examine the [Eq. 25]. As stated before, the liquidity of Bond ETFs instruments is the sum of two main components: the volatility of the

67


underlying index and the AP incentive to take part the creation and redemption process to keep the ETFs share price close to their NAV.

đ?&#x2018;&#x2020;đ?&#x2018;?đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ??¸đ?&#x2018;&#x2021;đ??šđ?&#x2018;&#x2013;,đ?&#x2018;Ą = đ?&#x203A;ź + đ?&#x203A;˝1 đ?&#x2018;&#x2030;đ?&#x2018;&#x153;đ?&#x2018;&#x2122;đ?&#x2018;&#x17D;đ?&#x2018;Ą_đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;đ?&#x2018;Ľđ?&#x2018;&#x2013;,đ?&#x2018;Ą + đ?&#x203A;˝2 đ?&#x2018;&#x2020;đ?&#x2018;?đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ??¸đ?&#x2018;&#x2021;đ??šđ?&#x2018;&#x2013;,đ?&#x2018;Ąâ&#x2C6;&#x2019;1 + đ?&#x203A;˝3 đ?&#x2018;&#x2030;đ?&#x2018;&#x153;đ?&#x2018;&#x2122;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2013;,đ?&#x2018;Ą + đ?&#x203A;˝4 đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;&#x201D;(đ?&#x2018;&#x2030;đ?&#x2018;&#x153;đ?&#x2018;&#x2122;đ?&#x2018;˘đ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x2013;,đ?&#x2018;Ą ) + đ?&#x203A;˝5 đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;&#x201D;(đ??´đ?&#x2018;˘đ?&#x2018;&#x20AC;đ?&#x2018;&#x2013;,đ?&#x2018;Ą ) + đ?&#x203A;˝6 đ?&#x2018;&#x192;đ?&#x2018;&#x;đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018;&#x2019;đ??ˇđ?&#x2018;&#x2013;đ?&#x2018; đ?&#x2018;?đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;&#x2013;,đ?&#x2018;Ą

(27)

In our inventory risk model, the spread of an ETF depends on the risk aversion of the AP, volatility of the underlying basket, the illiquidity of the basket of constituent securities and the trading intensity of the ETF.

đ?&#x2018;&#x2020;đ?&#x2018;?đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ??¸đ?&#x2018;&#x2021;đ??šđ?&#x2018;&#x2013;,đ?&#x2018;Ą is the đ??¸đ?&#x2018;&#x2021;đ??šđ?&#x2018;&#x2013;,đ?&#x2018;Ą low-frequency liquidity measure on day t, we scaled the percentage proxy by 100 to have the results expressed in basis points. đ?&#x2018;&#x2030;đ?&#x2018;&#x153;đ?&#x2018;&#x2122;đ?&#x2018;&#x17D;đ?&#x2018;Ą_đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;đ?&#x2018;Ľđ?&#x2018;&#x2013;,đ?&#x2018;Ą is the Volatility of the benchmark that the ETF tracks, it is computed as the daily standard deviation of index return over a rolling window of 30 days. đ?&#x2018;&#x2020;đ?&#x2018;?đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ??¸đ?&#x2018;&#x2021;đ??šđ?&#x2018;&#x2013;,đ?&#x2018;Ąâ&#x2C6;&#x2019;1 is one day lagged ETF spread. đ?&#x2018;&#x2030;đ?&#x2018;&#x153;đ?&#x2018;&#x2122;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2013;,đ?&#x2018;Ą is the 30-days price Volatility of the ETF. đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;&#x201D;(đ?&#x2018;&#x2030;đ?&#x2018;&#x153;đ?&#x2018;&#x2122;đ?&#x2018;˘đ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x2013;,đ?&#x2018;Ą ) is the ETF daily volume (it is the sum across all its listings: include also off book transactions). đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;&#x201D;(đ??´đ?&#x2018;˘đ?&#x2018;&#x20AC;đ?&#x2018;&#x2013;,đ?&#x2018;Ą ) stands for Assets under Management on day t, calculated as NAV multiplied by the number of shares outstanding. PriceDiscrepi,t is the difference between ETF price at end of the day and its NAV, it represents the incentive of the AP to participate to the arbitrage activities. It is calculated as (đ??żđ?&#x2018;&#x17D;đ?&#x2018; đ?&#x2018;Ą đ?&#x2018;&#x192;đ?&#x2018;&#x;đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x2013;,đ?&#x2018;Ą â&#x2C6;&#x2019; đ?&#x2018; đ??´đ?&#x2018;&#x2030;đ?&#x2018;&#x2013;,đ?&#x2018;Ą )/ đ??żđ?&#x2018;&#x17D;đ?&#x2018; đ?&#x2018;Ą đ?&#x2018;&#x192;đ?&#x2018;&#x;đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x2013;,đ?&#x2018;Ą â&#x2C6;&#x2014; 100.

68


Chapter VI. Results

The phantom liquidity of Bond ETF arises when the Liquidity mirage perceived by the public is not consistent with the premium paid by the investor during market distress scenarios. The result of regression analysis of higher liquidity in EU Bond ETFs market conflicts with the outcome of Event studies during high market volatility.

Using the sample of daily data that we collected from 03/11/2011 to 29/12/2017, we have 1750 observations for each 16 ETF. Before to start our analysis, we have to clean our data set. Most of the points that we discarded are from days without information or those who are missing some relevant statistics. Additionally, we adjusted the sample based on U.S. trading calendar, in this way we excluded the supplementary trading data from the European market.

In the [Table 7] we reported the summary statistics of the sample. The first thing that we can notice is that there is a negative correlation relationship between average trading volume and the average percentage bid-ask spread across the sample. Second, in our model we have only positive net inflow ETF, this result can be misleading. Since we are taking the most representative of each category, the ETFs that we are analyzing are the most efficient and less likely to default.

The most daily traded ETF is TLT (iShares 20+ Year Treasury Bond) with more than $1 billion transactions completed each day. Following the volume criteria, we can find the 69


two High Yield Bond ETF: HYG (iShares iBoxx $ High Yield) and JNK (SPDR Bloomberg Barclays High Yield). On the bottom of the list, the three European Bond ETFs are the least traded instruments, but they do not exhibit the highest percentage spread, this evidence supports the hypothesis that the liquidity can be only partially explained by volume exchanged.

In addition to the [Eq. 11] and [Eq. 12], we also used other straightforward proxies to capture as many possible dimensions of the Bond ETF Liquidity. These liquidity proxies are: High-Low, Bid-Ask premium and ETF premium on NAV. They are all well-known and relatively easy to compute. The analysis yields seven liquidity measure with 1404 point of observations.

High-Frequency Proxies are obtained by [Eq. 13], [Eq. 14] and [Eq. 15]. We combine the intraday data obtained from three different trading days to reflect actual trading processes. To improve the quality of the dataset and avoid the extremely noisy observations, we filter out the quotations before and after the trading hours and as well quotes where Bid price is higher than Ask price. Then we use high-frequency proxies as a benchmark to determine the best low-frequency proxy. The Correlation matrix is performed across liquidity estimates of eight Bond ETF. [Table 8]. The result is reported in the [Table 9].

70


The findings show that for Aggregate Bond ETFs, the best liquidity is merely the Bid-Ask Spread. Whereas for other categories, Amihud result the most appropriate measure. The results show no consistent evidence of a difference between US and Europe Bond ETFs liquidity measure. We believe that this outcome is mainly influenced by the scarcity of data and lack of regulation in the European Bond ETF Market.

Further analysis is summarized in the [Table 10]. In this chart, we split the previous high-frequency data on three scenarios (market volatility, “normal day”, interest rate volatility). Then we compare the data we collected at a granular level with the end-day reported statistics. On average European Bond ETFs trade less than half (45%, during market turmoil the average goes down to 40-43%) of its total daily volume on ‘the book’, meaning that there is still a robust private transaction channel in the European market between AP and institutional investors. Whereas US Bond ETFs transactions are made almost on the public exchange (94% of Volume traded), there is only a slight decline in high market volatility. (88-91%)

On Market distress days, European Bond ETFs display a positive premium (Avg. price on Book22 – Avg. price23). Investors who want trade European Bond ETFs in these days have to pay an additional liquidity component to enter into a position. Among US Bond ETF we do not find liquidity premium except for HYG. The high yield bond had on

Avg. daily price on “Book” = sum counter value of all transaction executed on a given day on total volume exchanged on the same day. 23 Trading benchmark calculated by dividing the total value traded (sum of price times trade size) by the total volume. 22

71


14/12/2018 a positive premium, contemporaneous to a negative delta24 and negative outflow. Since there was a high turnover (8.39%) for HYG, we can exclude the fact that the premium is caused by the illiquidity issue.

Based on the ETF structure. If the ETF price is higher (lower) than the Underlying basket NAV, AP can secure a profit if she goes short (long) on ETF and long (short) on underlying portfolio of bonds. Thus, the fund flow should be negative (positive). This outcome, by adjusting the turnover rate, is consistent with our sample. The only anomaly case is â&#x20AC;&#x153;LQDâ&#x20AC;? which exhibits irregularities in term of ETF flow adjustment and it can be a matter of further studies.

On the regression analysis, we split our sample again into eight categories. It is a reasonable choice since even they are fixed income products, they have entirely different features from each other. [Table 11] Given our model, our focus is mainly on the coefficient and sign of đ?&#x203A;˝1 đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018; đ?&#x203A;˝6

Generally speaking, the volatility of the index (đ?&#x203A;˝1 ) has a negative impact on the independent variable Y. When the volatility of the underlying basket increase, the spread on Bond ETF tend to decrease. This mechanism seems contradictory but if we take in account the role of AP, this outcome may result more rational. When there is a market

24

ETF price â&#x20AC;&#x201C; Fund NAV

72


distress, most of AP will absorb the shocks though the creation and redemption mechanism. The benchmark index volatility of Government (include municipal) and Emerging market bond ETFs do not have an impact on the spread, the outcome is consistent with the literature, most of Government and Emerging market bonds ETFs prices tend to diverge from the underlying NAV.

The price Discrepancy (đ?&#x203A;˝6 ) is the proxy that captures the APâ&#x20AC;&#x2122;s incentive for the traded ETF. We found that only in Inflation and Corporate Bond (IG and HY) ETFs segment, the AP are actively participating the arbitrage mechanism due to the price discrepancy. For Inflation Protection Bond ETF, one percent of increase in the price discrepancy will let the spread increase by 62.40 bp (or 0.63% of spread). TIP is a fund designed to track securities issued by the U.S Treasury that provide inflation protection to investors. If there is a consistent price discrepancy, it is likely that market participants may prefer to transfer temporarily their assets to this segment. A sudden increase of demand of this type of asset can wide the spread length. Comparing the Corporate bond ETF liquidity between EU and US. We can notice that given the same percentage of price discrepancy change, EU Bond ETFs react more efficiently, and the spread gap decrease more with respect to US bond ETFs. For instance, for IEAC (ISHARES Core Euro IG Corporate Bond), one percentage of increase of price discrepancy will lead to a decrease of 21 bps of spread. On the contrary, for US Bond ETFs, the spread gap decrease on average only 2 to 3 bps.

73


Appendix

74


List of Tables Table 1 â&#x20AC;&#x201C; Classification of ETP

Table 2 â&#x20AC;&#x201C; Key Differences in Fund Structures Table 2 - Key Differences in Fund Structures Features

Open-end Funds (Mutual Funds)

Closed-end Funds

Unit investment trust

Exchange-traded Funds

Pubblicaly Exchanged

No

Yes

Yes

Yes

Trasparency of Holdings Share Outstanding

Pricing

Holding remains the same Daily over time n of shares can chage at end-of-day Supply of Shares is Fixed n of shares can chage at end-ofSupply of Shares is Fixed through based on purchases and through an Initial public day based on creations and an Initial public ofering redemptions activities. ofering redemptions mechanism. Monthly or Quartely

Monthly or Quartely

all transactions are at end-of-day NAV

market determined

Secondary market through Fund companies market determined

Liquidity

End of Day

Intraday

Intraday

AuM approximately 18 trillion$ approximately 275 billion $ approximately 3 tillion $ source: Exchange Traded Funds: Strucutre, Regulation and Application of a new fund class - Elisabeth Hehn, Springer (2005)

Primary market: NAV Secondary market: market determined Intraday End of Day approximately 3.2 trillion $


JP Morgan KCG Morgan Stanley UBS Securities Virtu

Table 3 – Example of Common US and European APS US Bank of America Merrrill Lynch Citigroup Credit Suisse Deutsche bank Goldman Sachs and Co. Jefferies Soure: BlackRock, based on trading activilty in 2017.

Table 4 – List of Global ETF issuers

Europe Bank of American Merrill Lynch Susquehana International Securities BNP Paribas Citigroup Commerzbank Deutsche bank

Flow Traders Goldman Sachs Jane Street Societe Generale Bluefin Europe UBS

76


Table 5 – Sample

BND

US AGG

Segment

Europe

IEAC

Aggregate

IHYG

IGLT

JNK

VCSH

TLT

LQD

IEF

HYG

TIP

IG Credit

SHY

HY Credit EMB

Government

Emerging Market

MBB

MUB

Inflation Protection

Municipal

58%

1865

6458

0.002

0.0005

0.0015

0.0005

0.5887

0.0425

0.0305

0.089

0.24

0

0.11

2.42

Annual Turnover

0.84

2.24

1.01

2.24

Category Avg Turnover

7.76

6.12

8.43

5.71

8.45

8.4

12.45

7.88

AAA

AA

A

AA

Tables 6 A – Sample Details

42%

36

17406

0.66

0.75

0.9

3.5

2.79

4.04

3

B

A

Avg Credit Quality 4%

50%

0.0268

0.13

Avg Eff Maturity (Y) 21%

5%

46%

0.0007

0.0427

Avg Eff Duration

29%

7%

16%

2150

0.005

% Assets in Top 10 52640

47%

39%

1030

Expense Ratio

39017.4

29%

3%

35%

Number of holdings

Broad Market

37135.4

17%

13%

Top 10 instituion Institutions Mutual Funds on % total share Ownership Ownership held

Agency

AuM ($M )

U.S. Corporate

23878.5

Focus

22-09-03

Broad Market

21809.7

50%

Inception

BlackRock 22-07-02

U.S. Government U.S. Corporate

18231.9

Sponsor

iShares Core U.S. Aggregate Bo BlackRock 03-04-07 19-11-09

U.S. Corporate

Nome

AGG US iShares iBoxx $ Investment Gra 04-12-03

TIP US Vanguard

04-04-07

Ticker

LQD US BlackRock

VCSH US Vanguard Short-Term Corporate BlackRock

MUB US

IEAC LN

SHY US

MBB US

EMB US

JNK US

iShares 7-10 Year Treasury Bon

iShares National Muni Bond ETF

iShares Core EUR Corp Bond UCI

iShares 1-3 Year Treasury Bond

iShares MBS ETF

BlackRock

BlackRock

BlackRock

BlackRock

BlackRock

BlackRock

BlackRock

iShares JP Morgan USD Emerging BlackRock

01-12-06

03-09-10

22-07-02

22-07-02

07-09-07

06-03-09

22-07-02

13-03-07

17-12-07

SPDR Bloomberg Barclays High Y State Street 28-11-07

UK Government

EU Corporate

U.S. Government

U.S. Government

U.S. Municipals

EU Coporate

U.S. Government

U.S. Government

Emerging Sovereign

U.S. Corporate

2188.81

6057.58

7134.72

7676.52

9381.22

10263.4

11411

11830

11977.5

12248.8

7%

10%

87%

69%

22%

7%

39%

43%

28%

51%

7%

9%

4%

15%

0%

7%

9%

3%

4%

12%

16%

15%

32%

25%

54%

21%

46%

91%

67%

12%

44

451

34

12

3425

2204

60

501

389

960

0.002

0.005

0.0015

0.0015

0.0025

0.002

0.0015

0.0009

0.004

0.004

0.3075

0.063

0.635

0.997

0.0236

0.017

0.447

0.2888

0.0747

0.0449

0.36

0.54

0.24

0.77

0.08

0.31

0.66

7.48

0.32

0.46

2.15

0.38

0.54

0.54

0.29

0.99

0.96

2.93

1.19

0.75

10.96

2.8

17.68

7.62

6.19

5.32

1.89

3.92

7.21

3.62

15.49

2.93

26.02

8.36

5.58

5.73

1.94

5.89

11.49

6.22

BB

AAA

AAA

AA

BBB

AAA

AAA

BB

B

iShares TIPS Bond ETF

BND US Vanguard Total Bond Market ETF Vanguard

iShares iBoxx $ High Yield Cor

IEF US

iShares 20+ Year Treasury Bond

BlackRock

HYG US

TLT US

iShares Core UK Gilts UCITS ET

iShares EUR High Yield Corp Bo

AA

IGLT LN

IHYG LN

77


AGG US

Ticker

99.69

69.08

2.802

AAA

--

3.92

9.231

AA

--

43.91

--

11.76

42.75

A

0.03156

40.82

--

15.23

42.82

BBB

47.09

1.554

--

0

1.794

BB

40.14

--

--

--

--

B

10.47

--

--

0.01

--

CCC

--

--

--

--

CC

--

--

--

--

C

--

--

--

--

--

DDD

--

--

--

--

--

DD

--

--

--

--

--

SP

1.56

0.36

0.306

0.6009

NR

2.324199

--

0.796838

1.992666

0.765649

Material

7.525473

--

2.886933

13.150826

2.701241

Telecom

7.107136

--

1.902237

6.155656

1.796377

Consumer

16.408123

--

4.783181

18.774268

4.413106

Cons Non-cicl

--

--

Tables 6 B – Sample Details

LQD US

--

12.02

--

21.13

39.64

--

--

2.966

14.81

--

--

0.4752

0.04

--

--

0.6181

--

0.6973 0.01159

--

--

--

--

--

--

--

--

--

--

--

--

11.33

--

4.414

--

--

3.51931

--

--

1.31379

4.746995

5.007795

--

--

9.508178

--

--

--

--

--

8.30709

--

--

--

20.926877 13.335661

23.854444 12.794207

--

--

15.98155

--

--

--

17.860238

17.146933

--

--

--

--

0.17274 9.51665

0.26416 12.474

0.32534 13.3998

--

--

10.74068

9.165999

--

--

--

--

8.48286 7.081364

7.98058 6.397133

7.25357 40.912571 6.76079 7.311299

2.00086 2.059454

9.96647 32.981951 4.40723 10.153085

--

--

3.500541

3.37506

4.250531

--

1.869442

2.128178

99.985006

100.5456

0.39707

0.300975

--

99.855477

45.465493

0.148847

Govt

25.52

44.222

--9.356

--

SrvPub

1.28 --

--

0.5861

0.03121

Tech

33.87 ----

Indus

------

Financ

11.01 -----

49.324259

------

1.991763

------

1.85751

------

1.98736

--0.2125 ---

8.560862

100 44.2 0.05866 --

--

--

--

--

--

--

--

--

2.112942

0.000666

83.531352

--

--

0.98785

--

--

0.155288

99.738948

--

0.031213 --

--

--

--

--

Africa/Middl Asia e East Central

99.92874

--

--

Est Europe

0.171141

--

--

0.586045

--

--

--

--

--

--

--

--

--

0.173991 --

--

--

--

--

--

--

--

--

--

--

--

--

3.533623 --

-1.637904

--

0.003947 --

--

86.92895

1.502546 -99.93423

77.01955

--

--

--

0.08549 -3.75612 --

--

0.185144

--

--

0.011802

--

--

--

100.01619

2.277963

--

0.010583

0.96635

--

--

--

--

2.401559 --

7.600468

--

--

--

--

Geograhy

3.07886

APAC

11.951948

1.188714

Europe West

1.759468

6.22813 2.008609

--

41.94106

--

0.44315 4.82637

9.486431

JNK US

HYG US

100 34.76 5.142 --

--

EMB US 8.958 16.64 --

0.00145 3.07594

MBB US 0.541 57.38 --

10.82

--

14.52781

AMLAT

16.5084 1.547354 --

17.8893

N.Amer.

0.437519

16.71499 --

16.35785

94.849953

0.31926 4.61823 --

10.1713

Source: Bloomberg, ETF.com, Morningstar

more than 12%

85.459646 --

3.50657

0.479839 --

0.481222

-0.177075 11.951501

0.534005 --

--

--

SHY US 11.42 --

--

IEAC LN ---

---

--

----

----

----

-3.04 --

-23.83 --

-60.91 --

-1.398 --

----

99.97 ---

99.41 -99.96

IEF US TLT US 0.04

IGLT LN

Tables 6 C – Sample Details

--

Coupon Range

--

90.448078

Maturity

--

99.855477 --

11.291365

0% -4% 4% - 6% 6% - 8% 8% - 10% 10% -12%

IHYG LN

MUB US

0.00953 2.81931

Energy

Investment Sector

BND US --

1.327

Divsf

TIP US

Credit Quality

VCSH US

0

0.0002

--

83.91049

0.060887 12.572243

NA

0.013

--

87.554847 --

+10 Y

0.0069

--

0.0012

85.913628

26.053011

7-10 Y

0.0706

--

0.0025

11.119651

5-7 Y

0.0399

--

0.0284

0.0056 --

3-5 Y

0.346

0.0113

0.0296 --

--

128.852344 -100.016192 --

1-3 Y

0.1544

--

0.0699

0.0173 --

--

--

19.42569

0-1 Y

0.5703

0.0275

0.0802 0.0016

0.0084

0.0005

--

--

Ticker

0.7983

--

0.3067

0.0766 0.1487

--

0.0032

--

100.545602 --

--

--

0.0003

0.1945

0.2933 0.8497

--

0.0195

--

--

--

12.38

1

0.5599

0.3526 0.9916

0.1204

0.0046

--

--

99.985006 -99.738948 --

0.2103

0.7667

0.5626

0.8477

0.8937

--

--

--

9.876605

16.96

0.00951

0.034

0.4032 0.05068 0.0087

0.0757

--

--

--

--

36.82 17.05

0.306

0.032

0.2652 0.00159

35.49

0.00622

0.0261

--

--

0.0202

--

16.62 15.38

19.92

--

0.1448 24.57

1.484

1

0.1413

0.1454

--

25.01 29.21

0.1151

--

0.698

32.6

--

2.505

0.8587

0.3027

0.0233

21.71 20.47

--

3.1

1.769 16.9

--

13.94

0.03099

0.5318

0.4542

14.44 12.98

27.63

89.55

20.54

50.42

0.6213

0.5235

22.89 22.85

1.147

33.93 17.12

--

10.76

--

0.9422

22.51 20.01

31.62

5.819

36.26

20.38

99.97

99.41

0.02238

20.81 21.55 49.16

34.31

1.59

4.226

7.213

--

50.4

0.4671

0.9692

0.319 17.56 26.46

--

95.79

25.17

--

15

--

BND US 0.01645 49.09

22.04

0.04988

18.37

11.16

--

11.43

0.5598

TIP US 0.3415 6.62

11.59

EMB US --

12.15

--

31.17

LQD US

VCSH US --

0.2534

MBB US 0.181

--

--

5.031

AGG US

JNK US

HYG US

SHY US

7.311

--

14

29.72

16.405366 5.75248

IEAC LN

--

15.62

22.459444

MUB US

--

14.32

0.972601 12.232156

IEF US

8.355

0.171077

TLT US

4.792

0.001146 --

IGLT LN

IHYG LN

Source: Bloomberg, ETF.com, Morningstar

78


Table 7 â&#x20AC;&#x201C; Summary Statistics Price

Volume (mln)

Avg. Spread

0.15%

Percentage

Fund flow* (mln)

39,688

Absolute

23,054

0.162790838

25,690

Min 312,830

0.11%

Max 12,843,237

0.12%

4,514

Average 1,780,572

0.24%

9,515

20,157

p75 Median p25 St. Dev Min Max Average 109.3766 113.1890 104.0191 1.8025 108.3336 109.5156 110.6179

0.100241109

0.02%

AGG

0.279053647

0.14%

405,338 441,066

0.018342375

13,958,312 10,474,364

226,339

0.121814346

2,882,746 1,746,571

8,524,343

55,082

117.2348 124.2263 107.0621 3.7860 114.7507 117.7402 120.4054 82.3041 85.3215 79.1414 1.3676 81.2626 82.0519 83.4772 976,982

478,427

0.129843279

BND 114.4476 123.3261 105.1809 3.5838 112.0355 113.8687 116.0900

4,531,543

LQD TIP

54,233,298

7,211

741,553

9,332

6,771,985

0.14%

8,955

79.5959 80.9156 77.2255 0.8066 79.2277 79.8139 80.1699

0.91%

89.3719 96.1951 75.4331 3.8835 87.0000 90.1671 92.3746

1.09%

VCSH

1.022344786

HYG

1.179336950

1,094,409 95,370

39,340,199

58,350

7,387,269

6,108,959

12,674,274

38.5410 41.9065 31.4255 2.1814 36.8703 39.1964 40.4143

457,004

1,118,117

0.052465341

112.3242 123.1789 102.8344 4.3478 108.9286 111.8587 115.3222

JNK 107.7477 110.4557 102.9517 1.5259 106.6389 107.9555 108.9923

1,899

EMB

221,204

8,238

MBB

54,448,171

0.17%

1,479,493

0.15%

84.5345 85.3312 83.6014 0.2745 84.4112 84.4997 84.6771

0.139993972

7,050

0.234224328

2,441

82

0.26%

4,268

2,018,487

0.19%

119,808

0.03%

152.8861 174.6492 134.4409 10.5483 144.0872 153.1780 161.5118

SHY

0.202820976

IEAC

0.040735383

41,435 224,607

2,711,009

1,978,111

334,451

34,764,383

108.7030 114.1090 96.3927 3.4092 107.0335 109.5534 111.1177

46,221,042

0.280687161 1,566,815

MUB

8,648,956

5,302

105.1005 113.7512 91.3595 4.2350 103.1617 105.8628 107.7457

1,450

118.3514 143.1248 88.6955 11.1992 112.6331 120.2834 125.6052

0.18%

IEF

661

0.14%

TLT

16,234,280

0.229408837

204,378

0.024754931

29,979,714

59,418 696,325

130.7574 154.8914 108.5420 12.9966 118.2405 128.9593 142.9255 18.1345 19.6846 15.7967 0.9123 17.4670 18.3185 18.8899

IHYG IGLT

Source: Bloomberg * Net Inflow calculated from 03/01/2011 to 29/12/3017

79


Tables 8 – Example of Correlation Matrix AGG

Quoted

Quoted

1.0000

Effective

Weighted

Roll (X0)

Roll (-S)

Effective

0.1978

1.0000

Weighted

0.1845

0.0313

1.0000

Roll (X0)

-0.0190

0.0234

0.0123

1.0000

Roll (-S)

-0.0235

0.0039

0.0302

0.8690

1.0000

DailyAmihud

YearlyAmihud

High-Low

Spread

Premium

DailyAmihud

0.0488

-0.0603

-0.0281

-0.1464

-0.2091

1.0000

YearlyAmihud

0.0823

0.0080

-0.0039

-0.4145

-0.5453

0.3904

1.0000

High-Low

-0.0148

0.0102

-0.0285

-0.0226

0.0281

0.3031

-0.0210

1.0000

Spread

0.1047

0.0965

-0.0055

-0.0817

-0.1085

0.0342

0.1579

0.0285

1.0000

Premium

-0.0145

-0.0015

0.0000

0.1356

0.0978

-0.0798

-0.0420

-0.0736

0.0162

1.0000

BND

Quoted

Effective

Weighted

Roll (X0)

Roll (-S)

DailyAmihud

YearlyAmihud

High-Low

Spread

Premium

Quoted

1.0000

Effective

0.7282

1.0000

Weighted

-0.7661

-0.6951

1.0000

Roll (X0)

-0.1376

-0.1721

0.1551

Roll (-S)

-0.2379

-0.2767

0.2512

0.8792

1.0000

DailyAmihud

0.1215

0.1238

-0.1370

-0.0428

-0.0768

1.0000

YearlyAmihud

0.3474

0.3177

-0.3516

-0.0250

-0.0875

0.1887

1.0000

High-Low

0.0612

0.0717

-0.0675

-0.0382

-0.0349

0.3074

-0.1605

Spread

0.1557

0.1443

-0.1630

-0.0686

-0.0944

0.0381

0.0101

0.0754

1.0000

Premium

-0.0730

-0.1193

0.0683

0.0649

0.1315

-0.0913

0.0025

-0.1349

-0.0302

1.0000

1.0000

1.0000

Tables 9 – Summary Correlation Matrix

Segment

US

Europe

High Freq. Proxy

Low Freq. Proxy

Correlation HL

Quoted - Effective

Spread

10.47%

Amihud (Y)

6.03%

Spread

15.57%

High Low

7.17%

Quoted - Weighed

Premium

2.11%

IGLT

Quoted - Effective

Amihud (Y)

14.24%

IEAC

Quoted - Effective Quoted - Effective

Amihud (Y) Amihud (Y)

47.78% 28.43%

Quoted - Effective

Amihud (Y)

34.50%

Quoted - Effective

Roll (x=0)

7.24%

AGG Aggregate

Quoted - Effective

BND

Government IG Credit HY Credit

SHY

LQD HYG IHYG

80


Tables 10 â&#x20AC;&#x201C; Phantom Liquidity Aggregate VIX Scenario

AGG

IG Credit BND

LQD

HY Credit IEAC

Bid Ask Spread n Quote n Trade n Bid n Ask

0.17000 33,910 5,405 15,973 12,532

0.10000 22,685 6,262 7,950 8,473

0.02000 87,189 21,724 35,408 30,057

0.31000 4,319 1,086 1,890 1,341

Volume "Screen' Volume Tot Book Trading

2,177,712 2,495,653 87%

2,773,522 2,859,359 97%

6,869,655 7141854 96%

105,903 366,039 29%

109.0800 109.2734 -0.1934

81.6380 81.6381 -0.0001

120.1494 120.1537 -0.0043

391 235,241,954 52,702,302 53,481,362 101.48%

390 224,626,886 30,718,044 35,445,563 115.39%

109.25 -0.1700 185.725 52042.5 0.3569% 0.45%

81.66 -0.0220 81.66 36645.1 0.2228% 0.61%

Avg Price on Book Avg Price Premium Obs Min Sum Trade $vol Dealer Demand(sum bid $ Vol) Dealer Inventory (sum ask$ Vol) Inventory / demand Fund NAV Delta Fund Flow ($m) Market Cap ($m) Flow / Mkt Cap Tunover Normal

AGG

BND

HYG

0.01000 5,542 27 2,519 5,542

21,322,137 25,382,032 84%

113,584 232,614 49%

3,075,327 3980979 77%

121,324 286101 42%

131.0502 131.0496 0.0006

86.5303 86.5362 -0.0059

106.6703 106.6334 0.0369

84.1341 84.1341 0.0000

13.1055 13.1051 0.0004

391 825,384,600 151,419,867 117,834,386 77.82%

296 13,878,613 122,415,735 392,331,872 320.49%

391 1,839,157,360 413,321,133 326,151,851 78.91%

241 12,116,037 73,588,214 83,312,789 113.21%

389 258,739,964 32,211,182 14,338,082 44.51%

359 1,590,016 2,211,531,611 2,569,151,733 116.17%

120.22 -0.0706 -48.088 38941.5 -0.1235% 2.12%

130.975 0.0753 -32.2775 8412.96 -0.3837% 0.16%

86.54 -0.0097 43.27 18211.5 0.2376% 10.10%

106.7292 -0.0589 -40.8239 5262.1 -0.7758% 0.23%

84.13 0.0041 42.065 11448.7 0.3674% 2.26%

13.1008 0.0047 -3.2752 1632.51 -0.2006% 0.10%

IEAC

IHYG

0.16000 107,854 15,602 45,939 46,313

Volume "Screen' Volume Tot Book Trading

2,734,697 3,109,545 88%

1,950,473 2,095,382 93%

7,036,679 7,205,808 98%

14,003 25,407 55%

7,102,953 7,494,952 95%

109.3038 109.3042 -0.0004

81.6752 81.6776 -0.0024

121.3313 121.3313 0.0000

131.5459 131.5505 -0.0046

391 298,912,677 70,666,251 85,272,107 121%

389 159,305,253 31,284,384 42,253,066 135%

391 853,769,499 161,106,673 172,620,081 107%

109.38 -0.0762 0 52626.2 0.0000% 0.57%

81.72 -0.0448 16.344 37162.7 0.0440% 0.43%

121.39 -0.0587 -12.139 38969.4 -0.0312% 2.19%

Fed Hike Bid Ask Spread n Quote n Trade n Bid n Ask Volume "Screen' Volume Tot Book Trading Avg Price on Book Avg Price Premium Obs Min Sum Trade $vol Dealer Demand(sum bid $ Vol) Dealer Inventory (sum ask$ Vol) Inventory / demand Fund NAV Delta Fund Flow ($m) Market Cap ($m) Flow / Mkt Cap Tunover PHANTOM LIQUIDITY

AGG

BND

LQD

0.12000 571 30 354 187

HYG

0.10000 25,121 5,270 8,974 10,877

Fund NAV Delta Fund Flow ($m) Market Cap ($m) Flow / Mkt Cap Tunover

IGLT

0.01000 11,563 8,150 1,664 1,749

0.11000 49,534 8,101 21,880 19,553

Obs Min Sum Trade $vol Dealer Demand(sum bid $ Vol) Dealer Inventory (sum ask$ Vol) Inventory / demand

SHY

0.49000 1,259 131 417 711

LQD

0.04000 165,051 53,512 58,946 52,593

Bid Ask Spread n Quote n Trade n Bid n Ask

Avg Price on Book Avg Price Premium

Government IHYG

IGLT 0.01250 2,859 17 1,454 1,388

10,891 40,183 27%

1,086,476 1,112,435 98%

112,610 212,441 53%

87.3853 87.3853 0.0000

107.2169 107.2598 -0.0429

83.9652 83.9652 0.0000

13.14071215 13.1426 -0.0019

136 1,842,037 122,868,005 13,435,124 11%

391 620,693,971 197,948,310 209,824,776 106%

91 1,167,699 53,457,093 37,441,152 70%

353 91,226,182 22,416,672 10,240,313 46%

442 1,479,776 1,676,889,729 1,578,583,627 94%

131.523 0.0230 0 8692.02 0.0000% 0.02%

87.27 0.1153 -392.715 18638.2 -2.1070% 3.33%

106.9871 0.2298 23.5372 5128.83 0.4589% 0.02%

84 -0.0348 -8.4 11409.9 -0.0736% 0.80%

13.1652 -0.0245 0 1611.79 0.0000% 0.09%

HYG

0.02000 283 44 88 151

SHY 0.01000 8,625 1,382 3,423 3,820

IEAC

0.04000 77,480 24,976 25,517 26,987

IHYG

SHY

IGLT

0.0600 49,530 7,142 27,347 15,041

0.0600 18,266 3,917 7,238 7,111

0.0800 85,673 17,024 34,587 34,062

0.1000 1,089 27 200 862

0.0400 138,086 51,820 41,697 44,569

0.1000 468 58 136 274

0.0100 4,585 1,365 1,773 1,447

0.0100 3,451 27 1,803 1,621

3,885,081 4,126,106 94%

1,914,414 1,998,817 96%

6,284,983 8,022,249 78%

13,136 24,540 54%

17,538,881 19,337,043 91%

34,282 70,446 49%

1,448,679 1,471,872 98%

47,465 187,291 25%

109.3621 109.3643 -0.0022

81.7496 81.7498 -0.0002

121.5030 121.5034 -0.0004

131.5264 131.5227 0.0037

87.2272 87.2267 0.0005

107.1979 107.1888 0.0091

83.9801 83.9801 0.0000

13.20961925 13.2153 -0.0057

391 424,880,687 70,195,158 68,531,251 98%

391 156,502,636 18,513,126 28,227,210 152%

391 763,644,522 131,508,624 132,530,793 101%

184 1,727,731 48,763,095 54,531,255 112%

391 1,529,868,239 384,390,298 375,306,888 98%

139 3,674,959 25,520,456 52,274,167 205%

358 121,660,266 8,387,228 16,669,982 199%

408 626,995 1,884,905,842 1,644,268,672 87%

109.44 -0.0779 0 52640.6 0.0000% 0.81%

81.75 -0.0004 40.875 37217.3 0.1098% 0.42%

121.59 -0.0870 -12.159 39027.9 -0.0312% 1.96%

131.454 0.0721 0 8689.37 0.0000% 0.02%

87.23 -0.0028 -375.089 18224.8 -2.0581% 8.39%

106.9685 0.2294 -0.107 5130.15 -0.0021% 0.07%

83.97 0.0101 8.397 11414.2 0.0736% 1.07%

13.2461 -0.0365 9.9346 1631.83 0.6088% 0.04%

Book Trading

Flow as predicted

Inventory Incentive

81

Trading Hours (Local Time): LSE 08.00 - 16.30; NYSE 09.30 - 16.00


Agency

Municipal

Emerging

HY Credit

IG Credit

Inflat. Prot.

Government

Aggregate

Tables 11 â&#x20AC;&#x201C; Regression Results Intercept

VolatilityINDEX

Spread (t-1)

VolatilityETF

logVOLUME

logAUM

priceDISCREPANCY

R^2

AGG

coeff tStat Pvalue

147.51 3.2719 ***

-17.34 -3.1349 ***

0.051424 1.924 **

17.355 3.3407 ***

-7.7534 -0.89029

-20.259 -1.6826 *

14.55 0.59321

0.0242

BND

coeff tStat Pvalue

69.818 2.5399 **

-5.437 -2.6583 ***

0.37445 15.047 ***

4.8119 2.5072 **

2.0367 0.59051

-16.855 -2.6028 *

-7.081 -0.71226

0.168

SHY

coeff tStat Pvalue

1209.7 4.8425 ***

18.053 0.43603

0.04344 1.623 *

51.452 1.2317

5.768 0.41403

-321.68 -4.7391 ***

138.71 0.66075

0.0247

IEF

coeff tStat Pvalue

130.43 0.64345

-27.239 -1.661 *

-0.00058804 -0.021966

25.244 1.6622 *

-23.577 -0.92043

10.265 0.16899

-9.1612 -0.093811

0.00306

TLT

coeff tStat Pvalue

42.455 7.6958 ***

-0.21744 -1.8309 *

0.10131 3.809 ***

0.41864 3.5374 ***

-2.8277 -3.7547 ***

-6.1731 -8.0597 ***

1.1811 1.6299 *

IGLT

coeff tStat Pvalue

67.133 5.9469 ***

-0.79318 -1.1394

0.32457 12.83 ***

0.74408 1.0011

-1.4459 -1.6651 *

-15.611 -4.57 ***

-3.0507 -0.76871

TIP

coeff tStat Pvalue

492.82 6.1116 ***

-12.088 -2.8404 ***

0.19196 7.3186 ***

16.225 3.8782 ***

-8.1815 -1.1115

-106.12 -5.27 ***

62.397 3.022 ***

0.139

LQD

coeff tStat Pvalue

134.32 10.26 ***

-3.5843 -2.3115 ***

0.037846 1.4137

2.6928 1.8547 ***

-1.0094 -0.4174

-26.515 -7.7165 ***

-3.2663 -0.59481 ***

0.103

VCSH

coeff tStat Pvalue

8.1335 11.23 ***

-0.2542 -2.4392 ***

0.14254 5.3864 ***

0.18263 2.0637 ***

0.22616 1.5549 ***

-1.9496 -10.597 ***

-0.57486 -2.1714 **

0.204

IEAC

coeff tStat Pvalue

56.56 9.2437 ***

-0.69049 -1.0412

0.40629 16.695 ***

0.92041 1.4393

0.60117 1.3981

-13.646 -7.82 ***

-21.286 -6.5569 ***

HYG

coeff tStat Pvalue

278.76 9.2926 ***

-1.85 -4.5858 ***

0.17105 6.5256 ***

1.6207 5.1361 ***

-12.33 -8.4922 ***

-45.026 -5.9569 ***

-2.7433 -2.5443 **

0.227

JNK

coeff tStat Pvalue

157.6 7.7971 ***

-0.26184 -1.1384

0.44131 18.397 ***

0.27607 1.3848

-7.2858 -7.4078 ***

-25.242 -5.1365 ***

-2.0206 -2.4669 **

0.347

IHYG

coeff tStat Pvalue

66.647 11.319 ***

0.66704 3.9833 ***

0.17581 6.7217 ***

-0.15638 -1.1128

0.3519 0.7217

-16.31 -10.574 ***

-5.0579 -6.4662 ***

0.18

EMB

coeff tStat Pvalue

-50.321 -0.023643

154.99 1.4401

-0.015822 -0.58578

12.635 0.15421

-642.52 -1.878 *

857.04 1.3496

-22.938 -0.075893

0.0174

coeff tStat Pvalue

231.28 4.193 ***

-0.26458 -0.072733

0.11646 4.3817 ***

1.3568 0.73109

-12.046 -1.1842

-40.408 -2.2975 **

2.6121 0.57049

0.00714

coeff tStat Pvalue

4123.4 1.5351

-573.22 -2.7422 ***

-0.0010794 -0.040344

584.81 2.7284 ***

-421.17 -1.4898

-488.84 -0.75538

654.17 0.68962

0.0382

MUB

MBB

***, **, * denote sinificance at the level 1%, 5% and 10% level, respectively Sample observation: 1402

82

0.113

0.149

0.277


List of Figures

Figure 1 – Multiple source of liquidity for Fixed Income ETFs

Figure 2 – ETF Market Participants

83


Figure 3 – Secondary Market

Trading

Figure 4 – Creation of ETF Shares

84


Figure 5 – Creation and Redemption of ETF Shares

Figure 6 – Most ETF Activity Occurs on the secondary Market

85


Figure 7 – Fixed Income ETF AuM and Dealer Inventory

Figure 8 – Relationship Between ETF and Its Underlying

Figure 10 – Recent ETFs History Millestone of ETFs history S&P's Depositry Receipts (SPY) launched 1993

Bond ETFs debut (TLT, IEF, SHY, LQD) 2001 Vanguard enters the ETF market

2002

Smart Bera, Multi-factor ETFs enter the U.S. market

ETFs hit the $1 Trillion mark 2004

2010

2014

SPDR Gold Trust (GLD) launched

ETF hit the $2 Trillion mark

Source: Bloomberg, FactSet, Morningstar, ETF.com. Data as of August 2017

86

2015

ETF hit the $4 Trillion mark 2016 ETFs overtake hedge funds in assets under management

2017

2018


Figure 9 â&#x20AC;&#x201C; Multiple factors affect ETF liquidity

Figure 11 â&#x20AC;&#x201C; ETF asset and geography allocation

Fixed Income 18%

Other 16%

Other 2%

Japan 6% Great Britain 7%

United States 71%

Equity 80%

Percentage of total ETF net assets, August 2017

United States Great Britain Japan Other Total worldwide ETF assets

Category ETF net assets, August 2017 Equity Fixed Income Commodities Asset Allocation Alternatives Currency Total worldwide ETF assets

70.87% 7.68% 5.71% 15.7400% $4,798 billion

Sources: Investment Company Institute, ETFGI and ETF.com

87

80.09% 17.36% 2.01% 0.27% 0.20% 0.06% $4,798 billion


Figure 12 –Bond ETF allocations

Country

n ETF

AuM($M)

AuM(%)

United States

341

584507

70.16%

Great Britain

129

119419

14.33%

Canada

139

38241

4.59%

Germany

148

36885

4.43%

France

76

28825

3.46%

South Korea

37

5219

0.63%

Switzerland Hong Kong

22 6

4810 4498

0.58% 0.54%

source: ICI

Figure 13 – Model time framework

t=0

time

t=1

AP

t=2 creation / rendemption

Secondary Market

trade

trade

Figure 14 – VIX Index and Federal Fund Rate

Federal Fund Rate

VIX Index 14

1.6

12

1.4 1.2

10

1

8

0.8 6

0.6 4

0.4

2 0 01-09-17

0.2 01-10-17

01-11-17

0 11-08-17

01-12-17

Source: Bloomberg and Federal Reserve Bank of St. Louis

88

11-09-17

11-10-17

11-11-17

11-12-17


Model Proof 𝑛

𝑊2 = 𝑐 − 𝑞0 𝑃0 − 𝑞1 𝑃1 + (𝑞0 + 𝑞1 ) ∑[𝑢𝑖,2 − Փ𝑖 (𝑞0 + 𝑞1 )]

(18)

𝑖=1

𝑃1∗ = 𝐸1 (𝑢𝐵,2 ) − 𝑞1∗ = −

𝛬1 𝑞 1 + 𝛽𝛬1 0

𝛽𝛬1 𝑞 1 + 𝛽𝛬1 0

(23) (24)

Replacing 𝑃1∗ 𝑎𝑛𝑑 𝑞1∗ into the [Equation 15]

𝑊2 = 𝑐 − 𝑞0 𝑃0 − (−

𝛽𝛬1 𝛬1 𝑞0 )[𝐸1 (𝑢𝐵,2 ) − 𝑞 ] + [𝑞0 (1 1 + 𝛽𝛬1 1 + 𝛽𝛬1 0

𝛽𝛬1 𝛽𝛬1 − ][𝑢𝐵,2 − Փ𝐵 𝑞0 (1 − )] 1 + 𝛽𝛬1 1 + 𝛽𝛬1

𝑊2 = 𝑐 − 𝑞0 𝑃0 + (

𝛽𝛬1 𝛬1 𝑞0 ) [𝐸1 (𝑢𝐵,2 ) − 𝑞 ] 1 + 𝛽𝛬1 1 + 𝛽𝛬1 0

𝑞0 Փ𝐵 𝑞0 + (𝑢𝐵,2 − ) 1 + 𝛽𝛬1 1 + 𝛽𝛬1

(i)

(ii)

Differentiating with respect to 𝑞0

0 = 0 − 𝑃0 +

𝛽𝛬1 𝛬1 𝛬1 𝛽𝛬1 [𝐸1 (𝑢𝐵,2 ) − 𝑞0 ] − 𝑞 ) ( 1 + 𝛽𝛬1 1 + 𝛽𝛬1 1 + 𝛽𝛬1 1 + 𝛽𝛬1 0

𝛬1 Փ𝐵 𝑞0 Փ𝐵 𝑞0 + [𝑢𝐵,2 − ]− 1 + 𝛽𝛬1 1 + 𝛽𝛬1 (1 + 𝛽𝛬1 )2

89

(iii)


𝐸1 (𝑢𝐵,2 )𝛽𝛬1 𝛽𝛬1 2 𝛽𝛬1 2 𝑢𝐵,2 𝑃0 = − 𝑞0 + 𝑞0 + 2 2 1 + 𝛽𝛬1 (1 + 𝛽𝛬1 ) (1 + 𝛽𝛬1 ) 1 + 𝛽𝛬1 Փ𝐵 𝑞0 Փ𝐵 𝑞0 − − 2 (1 + 𝛽𝛬1 ) (1 + 𝛽𝛬1 )2

(iv)

𝐸1 (𝑢𝐵,2 )𝛽𝛬1 2𝛽𝛬1 2 𝐸1 𝑢𝐵,2 2Փ𝐵 𝑃0 = − 𝑞0 + − 𝑞 2 1 + 𝛽𝛬1 (1 + 𝛽𝛬1 ) 1 + 𝛽𝛬1 (1 + 𝛽𝛬1 )2 0

(v)

1 2𝛽𝛬1 2 + 2Փ𝐵 𝑃0 = [𝐸 (𝑢 )𝛽𝛬1 + 𝑢𝐵,2 ] − 𝑞0 [ ] 1 + 𝛽𝛬1 1 𝐵,2 (1 + 𝛽𝛬1 )2

(vi)

Imposing 𝐸1 (𝑢𝐵,2 )𝛽𝛬1 = 𝑢𝐵,2 = ∑𝑛𝑖=1 𝑢̅𝑖 the expected value of underlying basket is equal to the sum every component of its portfolio. 2𝛽𝛬1 2 + 2Փ𝐵 𝑃0 = 𝑢̅𝐵 − 𝑞0 [ ] (1 + 𝛽𝛬1 )2

(vii)

Recalling that 𝛬1 = 2Փ𝐵 + 𝐴𝜎𝐵2

𝑃0 = 𝑢̅𝐵 − 𝑞0 [

2𝛽(2Փ𝐵 + 𝐴𝜎𝐵2 )2 + 2Փ𝐵 ] [1 + 𝛽 (2Փ𝐵 + 𝐴𝜎𝐵2 )]2

𝑛

𝑃0 = 𝑢̅𝐵 − 𝑞0 𝛬0 = ∑ 𝑢̅𝑖 − 𝑞0 [𝐴𝜎𝐵2 + 𝑖 =1

90

(1 + 4𝛽Փ𝐵 )(2Փ𝐵 + 𝐴𝜎𝐵2 ) ] [1 + 𝛽(2Փ𝐵 + 𝐴𝜎𝐵2 )]2

(vii)

(25)


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The phantom liquidity of bond ETF  
The phantom liquidity of bond ETF  
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