market liquidity risk

Market Liquidity Risk

Market Liquidity RiskImplications for Asset Pricing, Risk Management, and Financial Regulation

Andria van der Merwe

market liquidity risk

Copyright © Andria van der Merwe, 2015.

All rights reserved.

First published in 2015 byPALGRAVE MACMILLAN®in the United States— a division of St. Martin’s Press LLC,175 Fifth Avenue, New York, NY 10010.

Where this book is distributed in the UK, Europe and the rest of the world, this is by Palgrave Macmillan, a division of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS.

Palgrave Macmillan is the global academic imprint of the above companies and has companies and representatives throughout the world.

Palgrave® and Macmillan® are registered trademarks in the United States, the United Kingdom, Europe and other countries.

ISBN: 978–1–137–39044–8

Library of Congress Cataloging-in-Publication Data

Van der Merwe, Andria. Market liquidity risk : implications for asset pricing, risk management

and financial regulation / Andria van der Merwe. pages cm Includes bibliographical references and index. ISBN 978–1–137–39044–8 (hardcover : alk. paper) 1. Liquidity (Economics) 2. Finance. I. Title.

HG178.V36 2015332.0415—dc23 2014048241

A catalogue record of the book is available from the British Library.

Design by Newgen Knowledge Works (P) Ltd., Chennai, India.

First edition: June 2015

10 9 8 7 6 5 4 3 2 1

Printed in the United States of America.

This book is dedicated to my parents for their love and support.

Contents

List of Figures ix

List of Tables xi

Preface xiii

1 Musings on Liquidity 1

2 Financial Crises and Liquidity Traffic Jams 19

3 Market Structures and Institutional Arrangements of Trading 39

4 Asset Pricing and Market Liquidity 75

5 Stories of Liquidity and Credit 115

6 Financial Regulation and Liquidity Risk Management 143

Notes 163

Index 187

Figures

3.1 Volume versus cost per trade by security class 493.2 The corporate bond credit default swap basis for

high-yield and investment grade bonds 694.1 The relationship between a stock’s excess return and

the bid-ask spread 794.2 The estimated mid, bid, and ask prices as a function of

finding a market maker 904.3 Mispricing and allocation of arbitrage capital 994.4 The liquidity level premium and the liquidity risk premium 107

Tables

3.1 US fixed-income market size and trading ratio 714.1 Investor types in the search-and-bargaining model 884.2 Liquidity premium with and without a funding crisis 1005.1 Change in the credit spread of a speculative-grade firm

in response to different size shocks in market liquidity 1405.2 Change in the credit spread of an investment-grade firm

in response to different size shocks in market liquidity 1406.1 Available stable funding factors for net stable funding ratio 1586.2 Required stable factors for net stable funding ratio 1596.3 Example of a bank balance sheet 160

Preface

During a recent visit to Paris, on my way to an important dinner appoint-ment, I was stuck in rush-hour traffic on the Champs Elysees. Despite efforts of looking for a way out of the misery, I was stuck, frustrated and powerless. . . . Traders and other financial market participant may experi-ence similar feelings of frustration when attempting to trade in an illiquid market. The flow of trade in an illiquid market is hindered. Trade in such markets are characteristically slow and costly or in the extreme case of absent counterparties, impossible. Market liquidity is an assumed charac-teristic of a well-functioning financial system and like traffic congestions we usually start paying attention to it when broken.

What exactly is market liquidity? Finance professionals typically define market liquidity as the bid-ask spread and upon closer examination raise questions about the need for a book about the subject. The bid-ask spread captures one dimension of market liquidity. As I argue in this book the implications of market liquidityon asset pricing, risk management and regulatory policies reach far beyond this one-dimensional measure of trading cost. A rethinking of market liquidity is needed.

My motivation for writing this book is threefold. First, the global financial crisis of 2007–2008 revealed flaws in our understanding of modern financial markets in general and market liquidity in particular. Much has been written about the evolution of the crisis, explaining what went wrong. This book view the crisis as a stepping stone for develop-ing a better understanding of the interconnectedness of markets and the common thread of market liquidity that binds particularly during times of distress.

We are entering a new era of liquidity regulation with the proposed changes under Basel IIII andthe implementation of the Dodd Frank Wall Street Reform and Consumer Protection Act in the United States and the European Market Infrastructure Regulation in the Europe. The

Preface

Preface

xiv Preface

full implications of these changes are still being monitored but theef-fectscan already be seen in reduced liquidity in the corporate bond mar-kets and unusual price dynamics in the U.S. Treasury bond market. The concepts I discuss in the book will enable you to understand the genesis of these new rules and form your own opinion about their necessity. The third motivator is the structural change brought about technological advancement—technology is changinghow and how fast market partic-ipants communicate with each other. Strategies such as high frequency trading are becoming part of the main stream vocabulary and it needs to be understood in the context of market liquidity.

This book will provide you with the asset pricing and risk management tools that are essential to navigatethenew market liquidityparadigm.

Tremendous gratitude goes to Chris Culp, from whom I took my first finance class as an MBA student at the University of Chicago, Booth School of Business and who has been a mentor and a friend for many years. Chris encouraged me to write this book, without that I would not have pursued this arduous task. I also want to thank Sanjay Bhasin who opened my eyes to the intricacies of real world trading. This book would also not be possible without the input from my colleagues at Compass Lexecon, particularly Rajiv Gokhale, David Ross, Neal Lenhoff, Jonathan Arnold, Jerry Lumer and Mike Keable who were instrumental in shaping my thinking about his important subject.

Liquidity is important and necessary. Liquidity is an assumed characteris-tic of a well-functioning market, but we only pay attention to it when it is absent—like the proverbial umbrella that is missing when it rains.1 What is market liquidity? Can we measure market liquidity? Can we manage market liquidity?

This question seems trivial (or even uninteresting) to the average per-son who equates liquidity with money in his or her pocket. An economist often associates liquidity with the availability of money or more specifi-cally with the actions of the central bank, succinctly defined by the for-mer chairman of the Federal Reserve, Ben Bernanke, in a 2008 speech on liquidity provision by the central bank: “Consistent with its role as the nation’s central bank, the Federal Reserve has responded not only with an easing of monetary policy but also with a number of steps aimed at reducing funding pressures for depository institutions and primary securities dealers and at improving overall market liquidity and market functioning.”2 Traders on the other hand equate liquidity with their abil-ity to buy and sell securities in financial markets.

However, none of these broadly defined, superficial understandings of market liquidity were sufficient to prevent the devastating and costly effects of illiquid capital markets we experienced during the 2007–2008 global financial crisis. Subsequent events force us to develop a deeper understanding of market liquidity and beg the question whether the mar-ket liquidity paradigm has shifted. Consider, for example, some ad hoc observations on liquidity that were made since the global financial cri-sis. Regarding the “flash crash” of May 6, 2010, the Economist reported, “This ‘hot potato’ trading generated lots of volume but little net buying.

1 Musings on Liquidity

2 Market Liquidity Risk

Traditional buyers were unable or unwilling to step in, and the depth of the buying market for e-minis and S&P 500-tracking exchange-traded funds fell to a mere 1% of its level that morning.”3

Mark Carney, the governor of the Bank of England, observed that in 2014, “The time to liquidate a given position is now seven times as long as in 2008, reflecting much smaller trade sizes in fixed income markets.”4 Carney also commented on the bizarre price swings in the US government bond market on October 15, 2014: “Fundamentally, liquidity has become more scarce in secondary fixed income markets.” These comments were made in 2014, almost six years after the financial crisis!

This chapter provides a historical perspective on what shapes our thinking about liquidity and highlights the premises of the neoclassi-cal financial edifice we need to redefine to accommodate a new market liquidity paradigm.

Historical perspective on trading, liquidity, and financial markets

The role of money and coins in antiquity

What constitutes liquidity has evolved and mutated over time. A com-mon theme throughout is that higher liquidity goes hand in hand with improved quality of information about the value of goods exchanged. Barter, the earliest form of exchange, was slow and uncertain partly because it provided no pricing transparency—if I trade a sword for a goat today, I may have to trade ten pigeons for the goat tomorrow and two swords for the ten pigeons next week. The value of bartered goods was purely driven by whether the seller was offering what the buyer needed. The notion of price or value was not well defined in bartered goods, and the process of exchange was slow.

Around 2000 BCE, silver and gold coins started to replace barter, giv-ing birth to the notion that liquidity is associated with money (or money supply). Trade for coins offered a common calibration, since the sword-maker who accepted five drachmas5 last month would probably accept about the same amount again this month. Around the late seventh cen-tury BCE, the Lydians created metal coins embossed with royal symbols

Musings on Liquidity 3

that guaranteed the weight and purity of the coin. Standardization made coins more useful for daily commercial transactions, which led to coins being widely accepted mediums of exchange. Everyone valued the coins equally; they could readily pay any cost. While barter was slow and costly due to the process involved in examining the attributes of goods typically used in barter exchanges, coins significantly lowered the information cost of transacting by their uniformity, which was the issuer’s guarantee of purity.

Over time, coins became emblems of great civic pride. The creation of coins and the management of their availability by ancient governments was one catalyst for the development of lively, centralized markets, such as the ancient agora, near the Athenian Acropolis, where people could meet.6 The ample supply of money lubricated trade and markets through-out the ancient world. For example, in the early years of the principate,7 the total Roman coinage per capita came to approximately 80 percent of the current US money supply.8

Ancient ports were awash with coins minted in different places. The first bankers were moneychangers—they worked behind tables—tapeza in Greek, and banc in medieval Italian.9 Bankers also connected business-men who needed funds to investors with excess funds. Since few Athenians could fully finance a trading voyage or the construction of a ship, bank-ers flourished and fulfilled the role of the first financial intermediaries who brokered transactions between several parties. Bankers knew who had the money and who needed it, and they were excellent managers of risk because of their intimacy with the market. Land was a typical form of collateral for a loan. William Shakespeare’s character Shylock in The Merchant of Venice exemplified the debauchery of early bankers whose collateral demand was no less than a pound of human flesh.

Migration away from money to financial market-based substitutes for money

Once metal-based money was introduced, and later in the era of the gold standard, liquidity was associated primarily with money. The fact that money is the most liquid asset and, that the cost of money to pay


for things is minimal (often zero) is conceptually attractive. But equating liquidity only with money is too narrow. In 1936, the British Depression-era economist and father of Keynesian economics John Maynard Keynes argued that investors will move from money to assets that are risky and less liquid only if they can expect to earn a reward from them, thereby extending the notion of liquidity away from money to the quality of asset. The market and financial economists after the 1929 stock market crash, consumed by rebuilding the economy, were not ready for liquidity to be managed as a separate risk. Incidentally, the main application of Keynes’s liquidity preference was monetary policy.10

In 1971, US president Richard Nixon suspended the dollar’s convert-ibility into gold to avert the mounting crisis of a large trade deficit and a costly war in Vietnam, ending the Bretton Woods system of fixed exchange rates. Floating currencies meant that capital could flow freely between countries, and it created the Milton Friedman world of free market eco-nomics. Liquidity was still thought of as money, but with the growth in Wall Street and global markets, at least in concept, liquidity gravitated toward financial markets.

Similar to earlier periods in which coins replaced barter and land was used as collateral for loans, innovation and financial development pushed back the liquidity frontier. The Peruvian economist Hernando De Soto, who revolutionized our understanding of transforming poverty into wealth, argued more generally that the transformation of “dead capital” into “live capital” is a key step in the development process.11

Money is desirable because it acts as “stores of value.”12 In other words, money serves as an effective cushion against pressing needs during peri-ods of potential future liquidity shortages, thereby mitigating the costs of dealing with uncertainty. Sir John Richard Hicks, one of the most influ-ential economists of the twentieth century and winner of the 1972 Nobel Memorial Prize in Economic Sciences, argued that other assets also have the capacity to act as stores of value.13 Hicks suggested that liquidity pref-erence, in the narrow sense of the demand for money, could be created in financial markets. This is exactly what the subsequent processes of financial development throughout the post–Bretton Woods period did—they cre-ated substitutes for money. Bank-based systems have naturally produced


such substitutes by offering deposit contracts and credit lines, which pro-vide an option to withdraw when liquidity is needed. Competition in the financial sector has spurred the growth of nonbank institutions that offer new products adapted to the liquidity preference of investors. An example of such innovation is the development of index funds during the 1970s. The first index fund available to individual investors, First Investment Trust, was sponsored by The Vanguard Group in 1976.14 Today, thou-sands of such funds are available to investors in the form of no-load index mutual funds and exchange-traded funds (ETFs).

The modern form of mortgages15 is another example of financial inno-vation that allows consumers to create stores of value, in the form of equity in their homes, when they borrow instead of finance their homes. Their commitment to reimburse interest and principal on their mortgages rep-resents a claim on their future income. This claim can be securitized and transformed into a store of value through the institution of mortgage-backed securities (MBS). The real estate mortgages of US households have grown from 15 percent of their net wealth in 1949 to 41 percent in 2001 due to various factors, such as financial innovation, increased risk tak-ing through high loan-to-value ratios, teaser rates and lack of refinancing penalties, and changes in legislation favoring homeownership.

The process of securitization is another example of how innovation creates a new market-based substitute for money. Securitization enables economic agents to obtain cash more readily against an array of future expected cash flows: from basic assets (loans, securities, and receivables) to other securi-tized products, such as subprime residential mortgage-backed securities, collateralized debt obligations (CDOs), or asset-backed commercial paper (ABCP). These developments provided market participants with more flex-ibility to allocate cash flows and manage risk associated with uncertainty, but they should have raised questions about the robustness of the market-based liquidity regime, as we will discuss in more detail in chapter 2.

The relevance of financial institutions and market structures

In a world of ideal, frictionless markets, every commodity would be readily available such that the aggregate supply equals the aggregate


demand. The general equilibrium economy suggested by American econ-omist Kenneth Arrow and French economist Gerard Debreu formalized the conditions needed for the aggregate supply to equal the aggregate demand. The so-called Arrow-Debreu model is central to the theory of general equilibrium that paved the way for the development of much of classical asset pricing theory.16 In this economy, savers can hold assets, such as equity, bonds, or demand deposits, which they can sell quickly and easily if they seek access to their savings. Firms also have permanent access to the capital markets to satisfy funding needs. The arguments sup-porting the general equilibrium economy do not provide any insight into the important roles of financial institutions, intermediaries, and banks in the provision of market liquidity.

Trading in markets is intertemporal and involves uncertain future value. Let me illustrate this point further. Why does it seem more rational for you to buy shares in Apple than shares in the Curl Up & Dye Salon in New Mexico? The simple answer is that you have never even heard of the Curl Up & Dye Salon in New Mexico. Assumptions of frictionless markets assume that all information is readily available and that investors agree on its implications, but they do not give much insight into the pro-cess by which information would be acquired and disseminated.

Gathering information is costly. In the real world, you may have to make a trip to New Mexico to learn more about the salon. If every investor pays this “fixed cost,” it would not be a very efficient use of economic resources. This creates an incentive for groups to form financial intermediaries to econo-mize on the cost of acquiring and processing information.17 By enforcing contracts and exchanging goods and financial services, markets and institu-tions ameliorate the problems created by real-world market frictions such as the cost of acquiring information.18 Financial markets develop precisely to overcome impediments to trade. The growth in the size and the devel-opment of complex market structures leads to less direct participation by individuals in financial markets toward more indirect participation of indi-viduals through various kinds of agents acting as financial intermediaries.

Although their array of products and services is more sophisticated than in antiquity, banks continue to act as financial intermediaries. Banks provide liquidity by acting as risk-sharing agents to insure against


depositors’ random consumption needs. Gorton (1990) explained the rationale for the existence of banks and deposit insurance as providing a riskless transaction medium to eliminate the need for uninformed agents to trade in assets whose returns are known by better informed agents. By issuing deposits, banks create “riskless” securities for trading purposes. In instances in which the bank asset risk is such that uninsured deposits cannot be made riskless, deposit insurance can replicate the allocation achieved with riskless private bank deposits.19

In the sixties, the economist Harold Demsetz laid the foundation for the economics of transacting. Demsetz developed arguments support-ing the role of market makers or financial intermediaries: they exist to satisfy the need for immediacy in delegated trading.20 The intermediary would be on standby, ready to transact whenever an order is placed, but he would also require compensation in the form of transaction costs for providing immediacy to trade. Demsetz proposed a simple one-period framework for thinking about market making, but actual trading mecha-nisms are far more complex. Market structures and institutional arrange-ments develop partly to accommodate this need for immediacy. How well markets achieve this goal depends on numerous factors, including the number of traders, the fragmentation of the market, the volume of trad-ing, the regulatory environment, access to capital, and many more, which we will touch upon in later chapters.

These arguments underscore the importance of financial market struc-tures and acknowledge their role in price discovery. As Nobel laureate Robert Merton remarked in his seminal paper on capital market equilib-rium, “To abstract from these [institutional] factors is to neglect an order-one influence on the short-run behavior of security prices.”21

Much of the ignorance of market structure in standard asset pricing the-ory can be traced back to the Arrow-Debreu general equilibrium model, which argues that the equilibrium price simply equates supply and demand. The work of French economist Leon Walras shed more light on price for-mation in the general equilibrium economy. According to Walras, security prices are formed through a process of trial and error in an exchange of assets between buyers and sellers at no cost and without need for immedi-acy. The novelty of Walras’s process is the presence of a fictitious Walrasian


auctioneer who is responsible for matching supply and demand through a process of preliminary auctions that are executed until demand perfectly equals supply, at which time the equilibrium price is achieved. The auc-tioneer lowers the prices for goods in excess supply and raises the prices of goods in excess demand until the markets clear. Traders have a chance to revise their orders, and no trade occurs outside this auction. The even-tual market-clearing price reached by the Walrasian auction is referred to as the general equilibrium price or fundamental value. The Walrasian auction exhibits the characteristics of a theoretically perfect market: no trading frictions or trading costs, perfect competition, and symmetric information equally shared by all participants. Immediacy is also not an issue in the Walrasian market, but we know that prices can contain a cost of immediacy of trade. The Walrasian auctioneer provides an intuitively appealing answer to the price formation question. In the real markets, a complex web of traders, dealers, and market makers facilitates the process of price discovery similar to the auctioneer. But the Walrasian auction still leaves holes in our understanding, because the general equilibrium auc-tion has no time dimension and no volume and capital constraints, which are relevant to price discovery in actual, real-world markets.

What does market liquidity have to do with it?

An essential ingredient for the functioning of financial markets is liquid-ity. Consider the following hypothetical example of an extremely illiquid market. An eccentric billionaire decides to sell his special model, purple convertible, but only on a Tuesday. The other eccentric billionaire wants to buy this exact model, but will only do so on a Wednesday. Now, unless you can convince these two billionaires to meet on the same day, no transaction will occur. This example illustrates the intertemporal nature of market liquidity. The two billionaires may also agree on the fundamen-tal value of the vehicle, but in this illiquid market, the transaction price is not well defined.

We define a liquid market as a market in which a large volume of trades can be immediately executed with minimum effect on price. In other words, the liquidity of the market can be recognized by how low


the uncertainties of the execution prices are. In addition, we consider “market depth,” which absorbs the price changes accompanied by trade execution, as an important factor in explaining market liquidity. In deter-mining “market depth,” we need to take into account the size take into account the size of the potential trade.

This definition of market liquidity culminates decades of economic and financial thought. In 1936, Keynes used terminology such as the “future volatility of market prices,” while the Nobel laureate Sir John Richard Hicks used the phrase “possibility of immediate execution of a transac-tion” to describe market liquidity. The author of several award-winning papers, Walter Bagehot in his 1971 paper “The Only Game in Town,”22 focused on factors such as the existence of adverse selection effects due to information asymmetry, the price impact of a trade, and the portion of trading cost that is set according to the pricing policy of the market maker. According to market microstructure, the branch of financial eco-nomics that investigates trading and the organization of financial markets at very short horizons, it is often the case that more practical concepts are introduced, such as the cost of changing positions (tightness), the trade size or thickness of the order book-profile (order book refers to a data set which provides traders with bid-ask prices and volume offered per price) required for changing prices (market depth), and the required period of time to recover from price fluctuation caused by a sudden shock or to reach a new equilibrium (market resiliency).23

Nobel laureate Merton Miller and his coauthor, Sanford Grossman, pointed out that market liquidity can be measured by looking at “the ability of executing trades under the current price quotes price- and time-wise.”24

Fisher Black, one of the authors of the famous Black-Scholes option pricing model, defined the liquid market on which much of finance the-ory rests. The following excerpt from Fisher Black’s seminal 1971 paper, “Towards a Fully Automated Exchange,” captures the multifaceted nature of market liquidity:25

The market for a stock is liquid if the following conditions hold: (1) There are always bid and asked prices for the investor who wants to buy or sell small amounts of stock immediately. (2) The difference


between the bid and asked prices (the spread) is always small. (3) An investor who is buying or selling a large amount of stock, in the absence of special information, can expect to do so over a long period of time at a price not very different, on average, from the current market price. (4) An investor can buy or sell a large block of stock immediately, but at a premium or discount that depends on the size of the block. The larger the block, the larger the premium or discount. In other words, a liquid market is a continuous market, in the sense that almost any amount of stock can be bought or sold immediately, and an efficient market, in the sense that small amounts of stock can always be bought and sold very near the current market price, and in the sense that large amounts can be bought or sold over long periods of time at prices that, on average, are very near the current market price.

The theoretical argument of no arbitrage

A necessary condition of equilibrium in financial markets is the principle of no arbitrage, which is defined as “the simultaneous purchase and sale of the same, or essentially similar, securities in two different markets for advantageously different prices.”26 According to this principle, you should be able to buy the cheaper gas at the suburban gas station and sell it at a higher price at the expensive gas station downtown to make a sure profit. The reason this is not a common form of employment is because such a transaction would entail risks and would require some capital invest-ment, both of which are assumed to be irrelevant in theoretical arbi-trage. Understanding the limitations of theoretical arbitrage arguments is important.

Under the no-arbitrage principle, prices should equal their funda-mental (equilibrium) values. An important corollary to the no-arbitrage hypothesis is the law of one price, which says that assets with similar payoffs should trade at similar prices. The other twin of the no-arbitrage theory is the efficient market hypothesis, which basically says that asset prices should change only in response to news about fundamentals; hence, asset prices follow a random walk. Arbitrage plays a critical role in the analysis of financial markets, because it ensures that prices converge


to fundamental values, essentially keeping markets efficient. But if all these theoretical arguments prove to be true, why are there still billionaire hedge fund managers?

The 2013 Nobel Prize winner Eugene Fama came to the rescue with his analysis of efficient markets. Fama explained that a large number of arbi-trageurs, each taking an infinitesimal position against such mispricing, transact in the financial markets to safeguard against differences between transaction prices of securities and their fundamental values.

Consider, for example, an arbitrage strategy involving the Standard & Poor’s (S&P) 500 Index and an S&P 500 futures contract. Assume that the risk-free annual interest rate is 8 percent. Further assume that the S&P 500 Index is trading at 2,000, and that the futures contract, due to expire in three months from now, is trading at 2,039. The 2 percent differ-ence in the price of the futures contract and the S&P 500 Index represents the time value of money or risk-free financing for three months—there is no arbitrage opportunity given these prices. But the Federal Reserve unexpectedly decreases interest rates by 1 percent, causing the S&P 500 Index to increase to 2,060. Further assume that the price of the S&P 500 futures contract increases to 2,040. This creates an arbitrage opportunity because the price of the futures contract is below its fair value. A trader can buy the futures contract and simultaneously sell the S&P 500 Index for a profit. Similar trades by other market participants cause the price of the futures contract to increase and/or the price of the S&P 500 Index to decrease. The futures contract will then converge to its fair value, and the arbitrage opportunity will disappear.

These types of apparent arbitrage opportunities exist and may even persist for some periods of time in financial markets. In theory arbitra-geurs have unlimited access to capital and are able short securities with-out limitations, making them essentially risk neutral toward fundamental values. Their collective actions should drive these types of relative mis-pricing to zero.

We argue that the theory rests on the critical assumptions of “unlim-ited access to capital” and “no risk.” If either of these assumptions is vio-lated, which is often the case in real markets and particularly in distressed markets, asset price distortions occur that are not well described by


classical asset pricing models. In chapter 4 we delve more deeply into the complexities of real-world arbitrage and the effects it has on asset pricing based on the seminal work on limits to arbitrage by Andrei Shleifer and Robert Vishny.27

Equilibrium asset pricing

A central tenet/paradigm of asset pricing is that the price of an asset is the expected discounted payoff.28 All asset pricing comes down to specifications of the discount factor that are useful for a specific application. There are two branches of asset pricing in which this central idea is applied: absolute and relative asset pricing. In absolute pricing, we value a stream of cash flows based on its exposure to fundamental sources of macroeconomic risk. The discount factor captures the investor attitude toward different macroeco-nomic states. Also referred to as equilibrium asset pricing models, this class of models assumes that aggregate market risks rather than individual risks are priced based on some notion of general market equilibrium.

Relative pricing determines the value of an asset given the price of some other asset. This approach uses as little information about macro-economic conditions as possible. To find the value of a McDonald’s ham-burger, absolute pricing starts thinking about how much it costs to feed a cow. Relative pricing looks at the price of a hamburger at Burger King. In finance, option valuation and corporate finance (the use of compa-rable investments to determine the required rates of return) are the prime applications of relative pricing methods. The Black-Scholes formula is another example of relative pricing—it expresses the option price given the stock and bond prices.

Asset pricing concepts can help investors and hedgers distinguish prop-erly between systematic and idiosyncratic risks. Using these basic prin-ciples, we can evaluate the components of market liquidity risk that should be the focus of asset pricing. For example, why would market liquidity be impacted by firm-specific idiosyncratic liquidity risk such as the CFO’s inability to properly manage future cash flow needs of the balance sheet?

The generality offered by absolute pricing should be weighed against the simplicity of relative pricing. Most models, however, are


a blend of absolute and relative approaches. Understanding the prin-ciples of equilibrium asset pricing is important because it gives insight into the limitations of pure theoretical models to real-world financial markets.

We can think of the asset price as the value or worth to the investor. The classical pricing theory argues that investors do not value money directly, but recognizes that it is the pleasure or “utility” of the con-sumption that money can buy what really matters. Specifically, people value money more if it comes sooner, and if it comes in bad times when they really need it, rather than in good times when they are already doing well. The following equation formalizes these considerations mathematically. Consider an asset with a single cash flow or payoff at a time in the future. The price or value of this asset to the investor is

P xt tc t

c ttE

u cu c

=( )( )

++β 1

1 .

The discount factor β is slightly less than one to reflect the “time-value of money” or, put differently, the investor’s preference for money sooner rather than later. The “utility function” uc decline as c, consumption, increases (a college student values $100 much more than the billionaire hedge fund manager).

We can rewrite this equation to reflect an investor’s optimal investment decision:

P u c E u c xt c t t c t t× ( ) = × ( ) × + +β 1 1 .

The true cost is the price of the asset (how many dollars the investor had to give up) times the value of a dollar (utility cost to the investor) uc (ct) at time t. The true benefit is the expectation Et of the dollar payoff xt+1 times the value of the dollar uc (ct+1) at time (t + 1), times beta, which discounts the future value of utility back to time t.

The fundamental value equation

Most financial economists do not think too much about consumption and utility functions, but find it more intuitive to think in terms of discount


factors. To establish a link between this thinking and the basic equation, we define a stochastic discount factor as the rate at which an investor is willing to substitute one unit of consumption now for one unit of con-sumption later:

mu cu ctc t

c t+

+=( )( )1

1β .

The discount factor is stochastic or random because of the uncertainty about future consumption and about the exact economic environment that will prevail in the future. Using this notion of a stochastic discount factor, we can recast the pricing equation in terms of an expected dis-counted payoff,

P E m xt t t t= [ ]+ +1 1 .

The randomness of the discount factor as an indicator of future pros-perity captures the intuition that assets that pay off well during adverse conditions, such as insurance, are particularly valuable and should be priced higher than assets that do not share this property.

Systematic versus idiosyncratic risk

Asset pricing is about risk and reward. Identifying risks and assessing the premium one earns for bearing risks are two central questions in asset pricing, and the point of theory is to provide the necessary quantitative tools to answer these questions. Risk managers tend to classify risk as market, credit, or liquidity. Financial economists tend instead to dis-tinguish between “systematic” and “idiosyncratic” risk. Not all risks are bad—you only earn a premium over the risk-free interest rate by taking on some risk.

A central idea in asset pricing is that only systematic risk generates a premium. Idiosyncratic risks are “not priced,” meaning that you earn no more than the interest rate for holding them. It is the job of risk manag-ers to get rid of idiosyncratic risks. To understand what “systematic” and “idiosyncratic” mean in context, we can rewrite the fundamental value equation as


PE x

Rm xt

t tf t t= [ ] + ( )+

+ +1

1 1cov , .

This equation says that the asset price is equal to the expected cash flow discounted at the risk-free rate, plus a risk premium. The premium depends on the covariance of the payoff with the discount factors. This covariance is typically a negative number, so most assets have a lower price than otherwise (or a higher average compensation for risk). Recall that the discount factor is an indicator of bad times. Most assets pay off well in good times. Thus, most asset returns and payoffs covary negatively with the discount factor. The converse case drives home the intuition. Insurance is a terrible investment. The average return is negative—you pay more in premiums than you receive, on average, in settlements. Yet people willingly buy insurance. Why? Because insurance pays off in bad times—the value of insurance is higher than predicted by the standard present value formula, because the covariance term is positive. Financial assets are “anti-insurance,” and it is this feature, and only this feature, that generates a risk premium and allows assets to pay more than the interest rate.

This equation has a dramatic implication: a risk may be very large in the sense of having a high variance, but if it is uncorrelated with the dis-count factor, its covariance is zero and it generates no premium. Its price is just the expected payoff discounted at the risk-free rate. The volatility of the asset’s cash flow per se is completely irrelevant to its risk premium.

The covariance of the cash flows of the asset with consumption is much more important to how buying the asset affects consumption—what inves-tors care about in the end—than the volatility of the asset’s cash flow.

Now we can really understand and precisely define “systematic” versus “idiosyncratic” risk. The systematic part of any risk is that part that is perfectly correlated with the discount factor. It is the part that generates a risk premium. The idiosyncratic part of any risk is that part that is uncor-related with the discount factor; it generates no premium.

In the modern version of the theory, systematic means correlated with the investor’s marginal utility. This is true no matter what “asset pricing model”—no matter what specification of the discount factor is correct.


A critical question for practical application remains: what data do we use for the discount factor m? The search to connect the discount factor to actual data has led to many “named” asset pricing models. These mod-els are just special cases of the fundamental value equation.

The single-factor CAPM provides a valuable conceptual framework for thinking about risk and return. The CAPM was developed by Nobel lau-reate William Sharpe29 and later extended by Fisher Black.30

The CAPM is one special case of the general theory. It specifies that the discount factor is linearly related to the market return. Hence it defines systematic risk for every asset by regressions of returns with the market portfolio return.

In the CAPM, a constant of proportionality, beta, tells us what the relationship is between the expected return of a particular asset and the expected return on the market portfolio:

E R R E R Rj fj

m f( ) ( ) ,= + − β

where Rj and Rm denote one-period arithmetic returns on asset j and the market, Rf denotes the risk-free rate, and where βj is the regression coef-ficient of the return on the market, or

β j

j m

m

R RR

=( )cov

var,

( ).

The CAPM is mathematically identical to a specification of the discount factor that is linear in the market return, rather than linear in the con-sumption growth. The CAPM discount factor model is a sensible approx-imation of the fact that most people are unhappy when the return on the market goes down. But it is clearly only an approximation. To derive the CAPM formally, you need to state assumptions under which a linear function of the market return is a completely sufficient indicator of good and bad times.

Keep in mind that the CAPM is not an alternative to the consumption-based model. It is a special case. Now, consumption surely goes down when the market return goes down, but, in the real world, other things matter as well. In addition, all investors in a CAPM world must hold the same portfolio of assets—the market portfolio.


Once dismissed as an institutional friction that is “assumed away” in complete markets, it seems that assets paying off poorly in times of poor market liquidity must pay higher average returns. Put differently, the dis-count factor is affected by liquidity. The marginal utility of a US dollar, delivered in the middle of a market meltdown, such as after the Russian bond default and LTCM collapse, may well have been very high.

Summary

Money is the most liquid asset, and the cost of using money to pay for things is minimal, often zero. If financial assets such as stocks, bonds, and loans shared these attributes, they could be circulated as money. But they do not share these attributes, their futures prices are uncertain, and buy-ing and selling them is costly. Each asset has its own liquidity profile that typically varies by how transparent its economic value is and how easy it is to communicate such features credibly to a large investor base.

The symmetric information-based asset pricing models do not work because they assume that the underlying problems of liquidity and price discovery have been completely solved. A security whose lowest returns tend to accompany unfavorable shifts in its marginal utility must offer additional compensation to investors for holding the security. Liquidity appears to be a good candidate for a priced state variable. It is viewed as an important feature of the investment environment and the macro econ-omy. In other words, if market-wide liquidity is indeed priced, it seems reasonable that many investors might require higher expected returns on assets whose returns have higher sensitivities to aggregate liquidity.

Yakov Amihud presented compelling evidence that market liquidity is priced in stock markets globally.31 Amihud’s study of equity market data from 45 countries shows that the average return on the most illiq-uid stocks are significantly higher than the average return on the most liquid stocks, after controlling for global and regional common risk fac-tors. The empirical results also find strong commonality in the liquidity premium—market liquidity should therefore be incorporated as a sys-tematic risk in asset pricing.

Overview of financial crises and latent risks

During noncrisis times, buyers and sellers usually show up in most mar-kets to trade, and they can all go on to do what they usually do: invest, hedge, and speculate. During such noncrisis times, regulators monitor financial markets using established policy frameworks.

However, during times of financial distress, these seemingly normal market operations may be wholly or partly impaired, for either shorter or longer time periods impeding the activities of buyers and sellers in the market and forcing regulators to question existing policies. A liquidity crisis culminates in the market’s inability to absorb transactions without violent price adjustments that are unrelated to fundamental value. In its most simplistic form, a liquidity crisis is characterized by an extreme widening of bid-ask spreads, and in the most extreme cases, it is charac-terized by the total disappearance of a market and the inability to trade. John Maynard Keynes eloquently captured the nature of the liquidity of a financial asset when he explained that an asset can only remain liquid if it is not simultaneously put to the test by all investors.1 It is precisely this paradoxical nature of liquidity that makes it so difficult to capture liquidity risks. In this chapter, we will investigate latent (masked) liquid-ity risks, which were exposed during the 2007–2008 financial crisis when contagion and irrational behavior toppled the rule of liquid financial markets.

The first latent risk arose because assets that were deemed liquid and safe turned out to be illiquid, thereby causing tremendous market dislocation. We will look at the behavior of highly rated money market mutual funds

2 Financial Crises and Liquidity Traffic Jams


(MMFs) in particular. A well-known example is the Reserve Primary Fund, a safe and conservative MMF that “broke the buck” after the failure of Lehman Brothers in September 2008. Since these investments are con-sidered safe and liquid, most market participants, including regulators, did not consider the liquidity risk posed by these funds.

The second latent risk is the market participant’s reliance on short-term funding. The consequences of refinancing risk were particularly detrimental for collateralized short-term borrowing when the market liquidity of assets used as collateral declined during the crisis. When the first losses on the subprime positions arrived in early 2007, investment banks, hedge funds, and many commercial banks were heavily exposed to refinancing risk in wholesale debt markets. This exposure was a key lever in generating, amplifying, and spreading the consequences of the collapse of money markets during the crisis.2

A third latent risk and a key characteristic of the 2007–2008 crisis was the failure of the interbank market to redistribute liquidity. Due to the central role of the interbank market, the disruptions and mispricing caused in part by an initial liquidity shock in relatively isolated corners of the market were transmitted across markets by financial institutions and trades that straddle markets.

Yale professor Gary Gorton observed that asymmetric information about the size and location of risk, and the accompanying fear of coun-terparty default, which was created by the complexity of securitization, was at the heart of the financial crisis.3 A relatively benign reduction in real estate prices in the United States increased the probability of default in structured asset-backed securities (ABS) and their related structured credit products. This was a credit event, but as portfolios became riskier, it raised questions about the value of these securities and this uncertainty transpired as market illiquidity in these securities. The market illiquid-ity caused difficulty in the financing and refinancing of these products, which led to funding illiquidity. The realization that these products span multiple markets increased the prospect of contagion. The market illi-quidity in structured products preceded funding illiquidity, which was detrimental to the interbank market, causing persistent dislocations in this market during 2008.

Financial Crises 21

Markets were used as a source of liquidity. Market participants who needed to sell assets could either sell other more liquid securities or sell securitized bonds at deep discounts. When liquid bankers first supply cash in exchange for assets, they create an imbalance in the system. They are increasing their holding of illiquid assets and reducing their holding of liquid assets. If these large, illiquid bankers are subsequently hit by a liquidity shock, they have even more assets that need to be sold, thus pro-ducing greater fire sales and reducing asset prices further.

Our focus is on the implications of the crisis for market liquidity and, in particular, identifying the potential modifications that are needed in the traditional asset pricing models. References to several excellent dis-cussions on the causes of the credit crisis in summer 2007 and its devel-opment from the relatively small subprime market shocks into a global liquidity crisis in 2008 are provided in the Appendix to this chapter.

Background—summary of securitization

Securitization of financial assets experienced a dramatic risk and fall in the decade before the recent financial crisis. According to data from the Securities Industry and Financial Markets Association, the issuance of structured finance products grew from $25 billion–$40 billion in the beginning of 2005 to a peak of about $100 billion by the second quarter of 2007. By the first two quarters of 2008, issuance dropped as low as $5 billion per quarter.4 Market participants failed to consider all the risks (particularly certain low-probability risks) associated with some types of securities, and therefore perceived highly rated bonds, including highly rated tranches of securitizations, to be less risky than they actually are. Credit ratings became the industry’s stamp of approval, and most partic-ipants did very little analysis beyond this one default-oriented metric. In general, there was limited understanding and mostly an absence of any measure of liquidity risks. Those who purchased the AAA-rated tranche of a collateralized debt obligation (CDO) had no reason to believe that the investment was risky. Due to the apparent safety of these securities, the demand from these participants was too high relative to the true risks in these securities. This outsized demand created incentives for financial


intermediaries to generate securities that appeared safe, thus increasing the supply of securities with hidden risks. During normal times, the per-ception of abundant liquidity is sufficient for markets to function. But during stressful times, the inherent instability and the ignored illiquidity of the markets are revealed to preclude normal functioning

An unexpected, often trivial shock to these securities caused their value to fall sharply as investors dumped them upon realizing their true risks. These dynamics can explain the malfunctioning of the collateralized debt market, particularly the run on repurchase agreements (wholesale fund-ing). Liquidity risk was not priced, but the market’s excessive reliance on ratings was also inherently flawed. The ratings process had several deficien-cies, such as the weakness of models and assumptions about correlations, poor understanding of the solvency of issuers and guarantors, conflict of interest emerging from repeated relationships between investment banks whose involvement in structured finance has become substantial and rat-ing agencies, and bundling of services by ratings agencies.

Systemic risks were exacerbated by the reliance on and use of short-term funding, because bank lenders are less likely to roll over their short-term funding if they observe adverse signals about the bank’s assets. When banks have assets in common, adverse signals about one bank are informative about other banks, leading to widespread funding freezes. The resulting lack of funding can cause many banks to have to liquidate assets inefficiently or even be bailed out.

Latent liquidity risks

Money market securities safety and liquidity: dressing like money is not equivalent to acting like money

The risks of MMFs have received relatively little attention in the academic literature because of their impressive record of price stability. MMF pro-spectuses must warn that “it is possible to lose money by investing in the Fund,” but before 2008, investors virtually never lost anything. Investors usually flock to MMFs during periods of heightened uncertainty due to their perceived safety. US Securities and Exchange Commission (SEC) Rule 2a-7 is specifically designed to restrict the credit, interest-rate, and

Financial Crises 23

liquidity risks that MMFs can take to maintain a stable net asset value (this rule does not apply to other types of mutual funds). The net asset value of MMFs is usually maintained such that the value of a share in the MMF is exactly $1. Before 2008, in the event that losses occurred despite these restrictions, the sponsors of the fund would absorb the losses to prevent the fund’s net asset value (NAV) from declining. The role of the fund sponsor is succinctly summarized in the classical reference guide on money markets, Stigum’s Money Market: “a money fund run by an entity with deep pockets, while it may not have federal insurance, certainly has something akin to private insurance . . . [and] that insurance is likely to prove adequate to cover any losses sustained by the fund.”5

When the Reserve Primary Fund, a AAA-rated MMF, lowered its share price below $1 (in other words, it “broke the buck”) due to its exposure to defaulted Lehman debt, it forced participants, including the regulators, to introduce a new paradigm that would prevent a repetition of these events.

A recurring theme in the narrative of the MMF performance during the 2007–2008 financial crisis was the devastating effects that ignorance of liquidity risk had.

The policy responses to the crisis in MMFs have recognized liquid-ity risk as a separate risk that needs to be explicitly managed over and above the default or credit risk. Researchers and policymakers are focus-ing on portfolios since many ex-ante risk proxies explain a large portion of the substantial variance in outflows during the crisis. Portfolio risk, as measured by gross yield, was a significant and economically impor-tant predictor of outflows during the run in 2008. It is interesting to note that studies have found that another possible indicator of portfolio risk—whether a fund had a triple-A rating—was of little use in predicting crisis outcomes.6 The discussion below closely follows the work done by the Federal Reserve Board of Governors economist Patrick McCabe.

MMFs that took more portfolio risk earned higher returns than their risky counterparts during normal periods of money market liquidity, but the riskier MMFs fared relatively worse during periods of low liquid-ity. As observed previously, the Investment Company Act documented a substantial increase in the portfolio risk of the Reserve Primary Fund


beginning in mid-2007, just over a year before its share price fell below $1, or “broke the buck”—a very unlikely occurrence for MMFs.

In a study on the effectiveness of the Federal Reserve’s Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility (AMLF), economists at the Federal Reserve Bank of Boston found that MMFs with greater Asset-Backed Commercial Paper (ABCP) exposures initially suffered larger outflows during the run in 2008, but recovered quickly with the announcement of the AMLF.7 Furthermore, the SEC in 2010 adopted amendments to Rule 2a-7 that impose further constraints on portfolio risk, including new liquidity requirements for MMF assets, “to make money market funds more resilient and less likely to break the buck as a result of disruptions such as those that occurred in the fall of 2008.”8

The SEC introduced an amended Rule 2a-7 that require MMFs to maintain a sufficient degree of liquidity necessary to meet reasonable foreseeable redemption requests and reduce the likelihood that a fund will have to meet redemptions by selling portfolio securities into a declin-ing market. MMFs generally have a higher and less predictable volume of redemptions than other open-ended investment companies. Their ability to maintain a stable net asset value will depend, in part, on their ability to convert portfolio holdings to cash to pay redeeming shareholders, with-out having to sell assets at a loss. The liquidity of fund portfolios became a critical factor in permitting them to absorb very heavy redemption demands in the fall of 2008 when the secondary markets for many short-term securities seized up.

The SEC added three new provisions to Rule 2a-7, which address dif-ferent aspects of portfolio liquidity. These will result in MMFs that are better able to absorb large amounts of redemptions. The three areas are discussed in more detail below: the general liquidity requirement, limits on the acquisition of illiquid securities, and minimum daily and weekly liquidity requirements.

General liquidity requirement

To comply with this general liquidity requirement, MMF managers should consider factors that could affect the fund’s liquidity needs, including the

Financial Crises 25

characteristics of an MMF’s investors and their likely redemptions. For example, some shareholders may have regularly recurring liquidity needs, such as to meet monthly or more frequent payroll requirements. Others may have liquidity needs that are associated with particular annual events, such as holidays or tax payment deadlines. A fund should also consider the extent to which it may require greater liquidity at certain times when investors’ liquidity needs may coincide. In addition, a volatility or more concentrated shareholder base would require a fund to maintain greater liquidity than a stable shareholder base consisting of thousands of retail investors.

Limits on acquisition of illiquid securities

Illiquid assets would impair a fund’s ability to meet redemption demands. Illiquid securities are defined as securities that cannot be sold or disposed of in the ordinary course of business within seven days at approximately the value ascribed to them by the MMF—this is essentially the market-based price.9 Illiquid securities are limited to 5 percent of total assets. Illiquid securities may be high-quality securities such as term repur-chase agreements, some time deposits, and insurance company funding agreements.

Minimum daily and weekly liquidity requirements

The SEC also adopted new liquidity requirements that mandate each MMF to maintain a portion of its portfolio in cash and securities that can readily be converted to cash. Daily liquid assets include cash (including demand deposits), Treasury securities, and securities (including repur-chase agreements) for which an MMF has a legal right to receive cash in one business day. Weekly liquid assets include the same assets, except that the fund would have had to have the right to receive cash in five business days rather than one. The SEC proposed to include Treasury securities regardless of their maturity in the liquidity baskets because they have been the most liquid assets during previous times of market stress. Indeed, empirical evidence shows that investor “flight to liquidity” that happens during times of uncertainty makes it easy to sell Treasuries


even in large quantities.10 The SEC also included agency notes (i.e., direct obligations of Federal government agencies and government-sponsored enterprises) as daily and or weekly liquid assets. Very short-term agency notes are likely to be sufficient under stressful market conditions to treat them as weekly liquid assets. The depth of liquidity in the secondary mar-kets for these securities was another motivating factor for their inclusion in the liquid asset basket.

MMFs typically invest a significant portion of their assets in repur-chase agreements, many of which mature the following day and provide an immediate source of liquidity. The amended Rule 2a-7 limits MMFs to investing in repurchase agreements collateralized by cash items or gov-ernment securities in order to obtain special treatment of those invest-ments under the diversification provisions of Rule 2a-7. This change is designed to reduce the risk that a MMF would experience losses upon the sale of collateral in the event of counterparty default.

Short-term debt, rollover risk, and pledgability of collateral

The amount of cash holdings and the maturity structure of long-term debt are important considerations of the liquidity risk management of firms. These choices involve a number of trade-offs for the firm. An advantage that short-term financing has for firms in good financial health is that it can readjust its maturity structure more quickly in response to changes in its asset value. This follows from the traditional method of choosing the maturity structure of debt, namely matching the interest rate sensitivity of its liabilities to that of its assets in order to shield a firm against changes in interest rates.11 However, the traditional view is agnostic to the fact that a firm can be exposed to sources of risk unrelated to changes in interest rates. For example, a firm with shorter maturity debt faces increased cost due to more frequent refinancing. Factors outside a firm’s control, such as a change in market conditions, could increase the cost due to higher interest rates at refinancing.

A firm could also face the risk that lenders may underestimate the con-tinuation value of the firm and not allow refinancing to take place, lead-ing to an inefficient liquidation of the firm, or the sale of important firm

Financial Crises 27

assets at fire-sale prices.12 One way to reduce the risk is to hold excess cash reserves, which can be expensive in practice.13

Two components of the short-term credit markets saw a significant growth in the decade before the crisis. The financial market relied exten-sively on ABCP and repurchase agreements to fulfill their short-term funding needs. Institutional cash pools played an important role in the growth of collateralized, short-term debt. Growing institutional cash pools created a demand for safe and liquid short-term debt that was met in part by securitization and other financial innovations.14

Below we discuss the latent liquidity risks in commercial paper and repurchase agreements. Both of these are examples of cases in which short-term debt is collateralized or backed by assets. Given that one wants to use assets as collateral, the amount of debt that can be issued depends critically on the value of the underlying assets or, in the words of MIT professor Bengt Holmstrom, and 2014 Nobel laureate Jean Tirole, what matters is the “pledgability of various assets.”15

Asset-backed commercial paper

Since the mid-1980s, banks have moved an increasing volume of assets off their balance sheets and funded them through ABCP programs. These bankruptcy remote “paper companies” issue short-term debt in the com-mercial paper market. Traditionally, ABCP programs financed receivables from nonfinancial companies, but over time they increasingly financed a wider range of assets, including highly rated mortgage-backed securities (MBSs) and other ABS. By the end of 2006, ABCP outstanding in the United States had grown to $1.1 trillion, larger than the amount of unse-cured (nonasset-backed) commercial paper outstanding. The short-term debt of ABCP programs consisted of ABCP with an average maturity of 90 days and medium-term notes with an average maturity of just over one year. The short-term assets are called “asset backed” because they are backed by a pool of mortgages or other loans as collateral. In case of default, the owners of the ABCP have the power to seize and sell the underlying collateral assets. The shorter term of ABCP caters to the pref-erences of investors since it allows them to withdraw funds at short notice to accommodate their own funding needs.16 Investors might suddenly


stop buying ABCP, preventing the vehicles from rolling over their short-term debt. To ensure funding liquidity for the vehicle, the sponsoring bank grants a credit line, called the “liquidity backstop.” As a result, the banking system still bears the liquidity risk from holding long-term assets and making short-term loans, even though these are usually off-balance-sheet entities.17

ABCP, as well as the underlying ABS trade in the over-the-counter (OTC) market in which volumes and prices of trade are opaque to any-one not directly involved in a particular transaction. Neither the inves-tors in such securities nor the arrangers anticipate frequent trading of these securities. In fact, buy-and-hold investors are typically the most active purchasers of these securities. As it became evident that the same ABS and the structured credit products referencing those securities were likely to perform worse than anticipated, valuations became more uncer-tain. Mounting concerns about the default risk of subprime and other mortgages, which started in summer 2007, caused a contraction in ABCP. Outstanding ABCP shrank by $190 billion (almost 20%) in August 2007, while yields soared and maturities shortened for new issues. Outstanding ABCP fell by an additional $160 billion by the end of the year.18

As is typical in the OTC market, the liquidity of the ABCP market is difficult to measure. However, one measure of illiquidity and investor risk aversion was the degree to which the average maturity of the paper shortened from August onward. Maturities of US ABCP range from one day to approximately three months. The average maturity in May 2007 was 24 days, with some 66 percent of outstanding ABCP having maturi-ties of less than nine days. The liquidity in the ABCP market declined as the severity of the financial crisis increased. By August 2007, the aver-age maturity was a mere 18 days, and 79 percent of outstanding ABCP had maturities of less than 9 days. Some normalization occurred in September, after injections of liquidity by the Federal Reserve, but as of October 2007, the average maturity was still lower than prior to the dis-ruption. It is also notable that the amounts outstanding of the ABCP, in which uncertainty about what backs the commercial paper is still present, have declined steadily, indicating funding liquidity using ABCP (versus nonasset-backed CP) is still impaired.

Financial Crises 29

Repurchase agreements

Another important trend in the period leading up to the crisis was invest-ment banks’ increased reliance on short-term repurchase agreements as a form of balance sheet financing. The growth in repurchase agree-ments was partly demand driven—repurchase agreements provided a vehicle whereby large institutional money pools, whose cash holdings far exceeded insured deposit limits, could lend short term to a financial insti-tution and receive collateral as protection. In a typical repurchase agree-ment, or repo, a firm borrows funds by selling an asset (collateral) today and promising to repurchase the same or a very similar asset at a later date. The lender changes a fee in the form of a margin or “haircut” to the borrower. For every $100 of collateral, an institution can receive $(100-x) in loans, with $x representing the “haircut” and (1/x) the allowable lever-age. The amount of the haircut represents the amount of capital that the borrower needs to put up. If the haircut is higher, the amount of capital needed to enter into such an agreement is higher, effectively translating into a higher cost of funding.

Precise estimates of the total size of the repo market are not available, but the order of magnitude is always in the trillions of dollars. The major-ity of this increase was due to overnight repurchase agreements, which roughly doubled from 2000 to 2007. Term repos with a maturity of up to three months have stayed roughly constant as a fraction of total assets. The greater reliance on overnight financing required investment banks to roll over a large part of their funding on a daily basis. Any reduction in funding liquidity could therefore lead to significant stress for the financial system.

According to a study by Gary Gorton and Andrew Metrick, the aver-age haircuts were near zero on most types of collateral before the onset of the crisis.19 The relatively cheap financing enabled institutions to borrow fast amounts with little capital investment. Haircuts started increasing at the time of the subprime panic, and continued a steady rise through-out 2008. How does an increase in haircuts drive the banking system to insolvency? Consider the following illustrative example: assume the size of the repo market is $10 trillion. If haircuts are zero, then the amount of financing that banks can achieve would equal the size of the repo market.


When the weighted-average haircut increase and reaches, say, 20 percent, then banks essentially have a shortage of $2 trillion since they have to finance the haircut with their own capital. In the early stages of the crisis, some of this amount was raised by issuing new securities. But as the crisis worsened into 2008, this shortfall in capital increased. In their seminal study, Gorton and Metrick reported that repo haircuts increased from approximately 10 percent in January 2008 to approximately 45 percent in September 2008 following Lehman’s failure.20 Furthermore, selling the underlying collateral drove asset prices down, which reinforced the cycle: lower prices, less collateral, more concerns about solvency, and ever-increasing haircuts.

As the crisis progressed, investors’ fears about the value of collateral snowballed to the point that lenders were no longer willing to provide short-term finance at historical spreads and haircuts.21 For every trillion dollars in the repo market for these nongovernment assets, each 1 percent increase in haircuts is equivalent to a $10 billion withdrawal of liquid-ity from the system; therefore, a 25 percent rise from July 2007 to the eve of the Lehman failure represents a large drain. Following the Lehman failure, the index rose by an additional 20 percentage points, including 100 percent haircuts (which is equivalent to no trade) for some assets. The drain of market-wide liquidity led to significant premia on even the safest assets.

The interbank market’s role in creating liquidity shortages

The interbank market plays a key role in the functioning of the financial markets. Central banks rely on interbank markets for the transmission of monetary policy. Banks rely on the interbank market to manage their liquidity by borrowing from and lending to their peers, a function that cannot be fulfilled by a bank’s access to customer deposits. Smooth func-tioning of the interbank market is therefore critical in maintaining stabil-ity of the broader financial market. In normal times, interbank markets are among the most liquid in the financial sector. Problems developed in the interbank market starting in August 2007, and as the financial crisis deepened, liquidity in the interbank market dried up by September 2008.

Financial Crises 31

Does the reason for the freeze in the interbank market provide any insight into market liquidity more generally?

While banks are privately informed about the risk of their own assets, they cannot observe the assets and the corresponding risk exposures of their counterparties. Lending banks in the interbank market accordingly faced counterparty risk stemming from the risk of the borrowing banks’ assets. Banks may need to realize cash quickly due to the demands of customers who draw on committed lines of credit or their demandable deposits. These banks can borrow from other banks with liquidity. If the borrowing is collateralized by risky assets, it may inhibit the bank’s ability to repay the loan in the interbank market. At the onset of the financial cri-sis in 2007, many banks used complex securitized products as collateral for borrowing. The 2007 crisis was initiated with a shock to the subprime mortgage market. As explained by Gorton, the complexity of these secu-rities made it impossible to know exactly where the risks were located, so all securitized asset classes were penalized with depressed prices and illiquidity in the interbank market, as reflected in the Libor-OIS spread, a standard measure of tensions in the interbank market:

Other asset classes only experience difficulties when there are prob-lems in the interbank market, starting in August 2007. . . . The Libor-OIS spread jumps in August 2007, and again when Lehman fails. Other securitized asset classes, with nothing to do with subprime, like credit card receivables, auto loans, and student loans, all move with the proxy for the state of the inter-bank market, not with the ABX. The key ques-tion for understanding the panic is: Why were non-subprime-related asset classes affected? Subprime mortgage originations in 2005 and 2006 totaled about $1.2 trillion . . . a large number to be sure, but not large enough to cause a systemic crisis. How was the shock turned into a panic? The shock was combined with asymmetric information about the locations and sizes of exposures to subprime.22

According to the European Central Bank, counterparty risk, amplified by asymmetric information about the location of risky assets, was a key fric-tion that caused persistent dislocations in the interbank market during the crisis.23 During times of distress, higher levels of risk cause adverse


selection in the interbank market. Suppliers of liquidity cannot distin-guish between safer and riskier banks, and do not have a way to protect themselves against banks with riskier assets. This negative externality on safer banks is so costly that they leave the market.

Liquidity is still traded, but the interest rate rises to reflect the pres-ence of riskier banks. When the risk of asymmetric information is high, interbank markets may break down, as occurred during the 2008 phase of the crisis. Banks with sufficient balance sheet liquidity preferred to hoard liquidity instead of lending it out, in order to avoid adverse selection. In the extreme, even riskier borrowers find the interest rates on interbank loans too high and prefer to obtain liquidity elsewhere. Depressed lend-ing and low prices for illiquid assets usually accompany high returns on holding liquidity.24

During the financial crisis, we observed several points of transition in the level and distribution of counterparty risk. Around the summer of 2007, it became clear that subprime MBSs were held in bank portfo-lios and bank-sponsored conduits. A further revision of expected default risk occurred after the collapse of Lehman Brothers in September 2008. This led to dramatic increases of unsecured rates and the excess reserves banks were required to hold, as well as the inability of massive liquidity injections by central banks to restore interbank activity. On November 9, 2008, the Financial Times reported, “Neither the recent massive money injections, the coordinated lowering of interest rates nor the use of public funds to recapitalize banks have done much to restart interbank lending. This action did not solve the underlying problem preventing interbank lending: extreme information asymmetry.”

The freeze in the interbank market highlighted the criticality of sym-metric information for market liquidity.

Liquidity crisis—implications for asset pricing

The latent liquidity risks had widespread consequences during the cri-sis, but their effect also extended beyond the 2008 period. The crisis exposed limitations in traditional financial economic theory that cannot be ignored. We discuss the limitations of economic argument by looking

Financial Crises 33

more closely at no arbitrage. We conclude this section with an overview of the challenges the crisis poses for asset pricing models.

Asset prices and the speed of arbitrage

No arbitrage forms the basis of much of modern asset pricing. It is the principle on which most derivatives pricing, such as the Black-Scholes model, is based, and it ensures that economic relationships between fun-damental securities hold. Many such relationships broke down during the crisis because many arbitrage relationships that had long been taken for granted by financial economists stopped working.

Textbook arbitrageurs operate in a frictionless economy that enables them to trade instantaneously when prices deviate from fundamental values. In contrast, the real-world convergence of price to fundamental values can be slow. In the event of capital dislocations, market partici-pants may be capital constrained, which prohibits them from “correcting” mispricing, thereby causing persistent price deviations from fundamental value.25

Professors Mark Mitchell and Todd Pulvino looked at various popu-lar arbitrage strategies implemented by hedge funds such as convertible bond arbitrage, whereby the payoff of a convertible bond can in theory be replicated using other traded securities. During the distress in the bond market in 2005, the prices of convertible bonds decreased relative to the price of the replicating portfolios because hedge funds arbitrageurs expe-rienced capital dislocations due to investor redemptions. During market downturns, when it could take time to raise funding (if raising capital is even possible), the prices of securities can become depressed relative to their fundamental value.

Due to various institutional frictions, securities with (almost) identi-cal cash flows can have very different margins. For example, consider a corporate bond and a credit default swap, both of which will provide an investor with credit exposure to a particular firm. Corporate bonds are considered “cash securities,” and typically involve an amount of upfront cash investment. To get credit exposure through a corporate bond, one must actually buy the bond for cash and try to fund it using a repurchase


agreement that uses a broker’s balance sheet. The secondary corporate bonds markets typically have low liquidity, and selling a bond, particularly during times of market distress, can be difficult and time consuming. A credit default swap is a derivative security that is “unfunded” in the sense that it requires relatively minimal upfront capital. A small margin may be necessary only to limit counterparty risk. Under theoretical no-arbitrage arguments, the difference in the yield spread on the corporate bond and the premium on the credit default swap, referred to as the basis, will be zero. Professors Nicolae Gârleanu and Lasse Pedersen studied the nega-tive basis that developed and persisted for a period of months between credit default swaps and corporate bonds during the crisis. The persis-tence of the nonzero basis constitutes a failure of the principles of no arbi-trage, and as explained by Gârleanu and Pedersen, it can be traced back to the difference in margins requirements on corporate bonds and credit default swaps.26 For example, assume a 10 percent cost of capital and fur-ther assume a margin of 5 percent on the credit default swap trade, and a margin of 25 percent on an investment-grade corporate bond (which was a typical level during the crisis). The direct effect of this margin difference between credit default swaps and corporate bonds is 10% × (25%-5%) = 2%, which is close to what was observed empirically during the crisis.

Arbitrageurs often invest other people’s money, resulting in a principal/agent problem that is exacerbated in market downturns. If liquidity provid-ers face external capital shocks, they become liquidity demanders, leading to a situation in which there are only sellers and no buyers in the market. Rather than increasing investment levels when prices dip below fundamen-tal values, they may sell cheap securities, causing prices to decline further. As a result, liquidity spirals could cause prices to drop. New capital typi-cally arrives slowly because information barriers separate investors from money managers, who face the prospect of investor redemptions as a result of the decrease in the value of managed funds. The increase in margin requirements further increases the cost of raising more capital.27 It is costly to maintain dormant capital, infrastructure, and talent for long periods of time, while waiting for profitable opportunities. Other agents may lack both the infrastructure and information to trade.28 Markets become highly illiq-uid when liquidity providers are constrained and traders demand higher

Financial Crises 35

expected returns as compensation for this lack of liquidity.29 The result is that profit opportunities for unconstrained firms can persist for months. These observations are evidence that real-world frictions impede arbitrage and that the “speed of arbitrage” needed to restore the market depends crit-ically on the availability of capital to participants.

Limitation of asset pricing models

Asset illiquidity raises a number of key asset pricing issues. With virtually no trades and no market prices in some markets during the crisis, market participants had to rely on models to assess valuations—but the models had been developed during a time of liquid markets and had not been tested in adverse conditions, casting doubt on the accuracy of their valu-ation estimates. We next discuss some of these limitations due to naiveté about particular aspects of market liquidity that affect asset prices.

Nonmarketability of illiquid assets

Longstaff explains the risks created by asset illiquidity as nonmarketability of the asset. Illiquid assets cannot be bought or resold immediately.30 The implications of illiquidity and nonmarketability of assets were brought to the forefront during the crisis when markets for securitized products ground to a halt. The complexity of many structured securities combined with the inadequate information and disclosure that typically accompany trading in OTC markets caused markets to be more illiquid than they would otherwise be. Then-Chairman of the Federal Reserve Ben Bernanke observed in a speech at the Economic Club of New York on October 15, 2007, “Moreover, in the absence of an active syndication market for the leveraged loans they had committed to underwrite and without a well-functioning securitization market for the nonconforming mortgages they had issued, many large banks might be forced to hold those assets on their books rather than sell them to investors as planned.”31 Hedge fund investors were especially affected and left with completely illiquid assets as many funds suspended redemptions.

The trade price of a liquid asset can be greater than the simple present value of cash flows. As highlighted by many examples in this chapter, the


nonmarketability of an illiquid asset reduces its value to much less than that of a liquid asset with the identical cash flow dynamics. A recurring theme is that it is not directly dividend cash flow that matters but rather the consumption steam that asset ownership generates. Furthermore, liquidity is not symmetric, and has the effect of making liquid assets more valuable and illiquid assets less valuable relative to the equilibrium prices.

The influence of marketability of a security on asset pricing demands attention after the experience of the 2007–2008 financial crisis, but it is not a new idea. In 1959, Professor Lawrence Fisher presented an impor-tant hypothesis about the determinants of [the] yield spread, also referred to as the bond risk premium.32 Fisher showed that the average risk pre-mium on a corporate bond depends on two factors, the creditworthiness of the lender (or the default risk) and the “marketability” or liquidity of the security.

Empirical studies provide considerable evidence that differences in liquidity can have major effects on the pricing of corporate bonds or, equivalently, on their required returns. Trading is costly to investors, and rational investors demand compensation for bearing liquidity (trading) costs. The price of corporate securities changes in response to changes in liquidity due to the increase or decrease in the return demanded by inves-tors. Accordingly, the liquidity of a company’s bonds can directly affect capital structure decisions as well as the timing of debt and equity issu-ances. A case in point is the refinancing risk posed by short-term debt, which we will discuss in more detail in chapters 5 and 6.

The importance of correlation risk

Most market participants recognized even before the crisis that correla-tion increases during a crisis, that is, markets that are seemingly uncon-nected in normal times can become connected in times of financial stress. However, the widespread effects of this increased correlation were surpris-ing. During the recent crisis, many companies simultaneously defaulted, were downgraded, or experienced severe capital constraints.

The demise of the subprime market provided an exogenous mar-ket shock, which had dire consequences. An interesting analogy of this dynamic is the horizontal oscillations of the Millennium Bridge of London

Financial Crises 37

that forced the bridge to close for further tests three days after its opening to the public.33

Adverse shocks reduced the amount of capital available to financial intermediaries, which lowered their ability to make deep liquid markets in which trades could be executed at narrow bid-ask spreads with small price impacts. As liquidity in the market worsened, trading falls and the short-term cash inflows of these institutions drop too, since most of their profits arise from market-making revenues. The worsening of short-term cash inflows of intermediaries, and in turn, their funding ability, limits their liquidity-provision role even further, giving rise to a downward spi-ral and a sudden drop in both funding liquidity of intermediaries and the market liquidity they provide. To summarize, if an asset shock is great enough that the capital position of a sufficiently large number of inter-mediaries is rendered constrained (or close to being constrained), then there may be a sudden drying up of both funding liquidity and market liquidity. These dynamics were amplified by a herding behavior of market participants, such as increased margin calls and the need for the forced sale of assets.

This link between funding and market liquidity risks implies that prices in capital markets effectively exhibit two “regimes.” In a normal regime, intermediaries are well capitalized and liquidity effects are minimal: the prices of assets reflect fundamentals and no (or little) liquidity effect. Thus, the correlation across asset prices in these times is also driven primarily by the correlation in the fundamentals of underlying entities or risks.

In the illiquidity regime, intermediaries are close to their funding or capital constraints, and prices now reflect the “shadow” cost of capital to these intermediaries, that is, the cost they suffer from issuing an addi-tional unit of funding capital to undertake a transaction. In economic parlance, there is “cash-in-the-market” pricing, and the liquidity posi-tion of market participants in a particular security affects the price of that security. Since this liquidity effect (illiquidity discount) is related to intermediaries’ capital rather than to the fundamentals of the security, it affects the prices of securities traded by these intermediaries across the board, inducing a correlation in securities’ market prices over and above the one induced by the fundamentals.34


Appendix

Ackermann, J. 2008, “The Subprime Crisis and Its Consequences,” Journal of Financial Stability, Vol. 4, pp. 329–337.

Acharya, V.V. and M. Richardson, 2009, “Causes of the Financial Crisis,” Critical Review, Vol. 21, Nos. 2–3, pp. 195–210.

Gerardi, K.S., A. Lehnert, S. M. Sherland, and P. S. Willen, “Making Sense of the Subprime Crisis,” 2009, Federal Reserve Bank of Atlanta Working Paper 2009–2 (February).

Gorton, G. 2008, “The Panic of 2007,” Prepared for the Federal Reserve Bank of Kansas City, Conference, Jackson Hole, WY.

Gorton, G. 2009, “Slapped in the Face by the Invisible Hand: Banking and the Panic of 2007,” Prepared for the Federal Reserve Bank of Atlanta’s 2009 Financial Markets Conference: Financial Innovation and Crisis, May 11–13, 2009.

Gorton, G. 2009, “Information, Liquidity, and the (Ongoing) Panic of 2007,” American Economic Review, Vol. 99, No. 2, pp. 567–572.

Gorton, G. 2008, “The Subprime Panic,” National Bureau of Economic Research, Working Paper 14398.

Gorton, G. B. 2010, Slapped by the Invisible Hand: The Panic of 2007 (Oxford: Oxford University Press).

Gorton, G. B. and P. He, 2008, “Bank Credit Cycles,” Review of Economic Studies, Vol. 75, No. 4, pp. 1181–1214.

Gorton, G. and A. Metrick, 2010, “Haircuts,” Federal Reserve Bank of St. Louis Review (November/December), pp. 507–520.

Gorton, G. and A. Metrick, 2012, “Securitized Banking and the Run on Repo,” Journal of Financial Economics, Vol. 104, No. 3, pp. 421–560.

Gorton, G. and A. Metrick, 2012, “Getting Up to Speed on the Financial Crisis: A One-Weekend-Reader’s Guide,” Journal of Economic Literature, Vol. 50, No. 1, pp. 128–150.

3 Market Structures and Institutional Arrangements of Trading

Introduction

Markets match buyers and sellers, and enable the formation of security prices. The viability of a market therefore depends on how well buyers and sellers are matched and how accurately the trade price evolves. Matching implies the provision of liquidity. In classical finance theory, this con-jures an image of the Walrasian auctioneer letting the hammer down on the final auction price. In real-world financial markets, the role of the auctioneer is fulfilled by the market maker or financial intermediary. But liquidity also arises from other aspects of the trading mechanism that are determined by the institutional framework and structure of a particular market, which in turn are determined by rules and regulations that gov-ern trade and interaction between market participants.

Price formation involves the incorporation of new information into security prices. Price formation necessarily requires us to consider the tension between participants with information (“informed traders”) and participants without information (“uninformed traders”). On the one hand we have the institutional framework that determines how informa-tion becomes public, and on the other hand we have the fundamental features of the particular security that are more or less revealing about its true value. Money is the most liquid asset, and the cost of using money to transact is typically zero. The “market” for money is liquid and facilitates orderly and immediate transactions because money has a unique charac-teristic: the price of one dollar is always equal to one dollar. There is there-fore no uncertainty about a dollar’s fundamental value. Residential real


Market Structures


estate, in contrast, is relatively illiquid, precisely because of legal impedi-ments involved in buying or selling homes, such as mortgage origination fees and title insurance, appraisal costs and the cost of home inspections. In addition, a relatively limited number of buyers for a particular home prevent instantaneous transfer between buyers and sellers. Financial assets such as stocks, bonds, and derivatives fall somewhere between these two extremes—where and how these securities are traded depends on the process of trade and the institutional arrangements and regulatory policy enabling this process. In other words, it depends on the underlying structure of the market.

The financial system is made up of the central banks, dealer banks, money market funds, major institutional investors, repo clearing banks, over-the-counter (OTC) derivatives, central clearing parties, and exchanges connected via a complex, constantly evolving set of institu-tional arrangements that allow participants to interact competitively to trade. A market can occupy a physical location such as an open out-cry trading floor. A market can also be an electronic access network in which market makers use telephones or screen-based systems to arrange bilateral trades with each other and with customers, or it can be totally machine operated, as is prevalent in high-frequency trading. The con-nectors include lending facilities offered by central banks to each other and to dealer banks, tri-party repo and clearing agreements, OTC derivatives, master swap agreements, prime brokerage agreements, and settlement systems arranged through Fedwire, operated by the Federal Reserve Banks, the Clearing House Interbank Payments System (CHIPS), CLS Bank, the Depository Trust Company, and other major custodians and settlement systems.

The financial crisis of 2007 to 2009 exposed many weaknesses in the structure of financial markets and heralded in a new era of regulatory ini-tiatives. In addition, rapid advances in technology, enforced or enabled by regulatory changes, led to widespread, automation and electronic com-munication systems that paved the way for the electronic trading systems that are challenging the dominance of floor-based exchange trading. High-frequency trading, or the use of powerful computers to analyze and execute trading opportunities at speeds of nanoseconds, which existed as

Market Structures 41

a computing experiment only five years ago, now accounts for 60 percent or more of trades in equities and futures markets in the United States.

However, even though the underlying rules, processes, and roles of dif-ferent agents are changing, the fundamental role of the market has not changed. In its most basic form, a market is an aggregation of buyers and sellers who interact according to a set of rules that determine what they can do, how fast they can do it, and what information is public versus pri-vate. The increased reliance on computerization and artificial intelligence in finance has fundamentally transformed the financial markets into faster, larger, more global, more interconnected, and less human entities than ever before.

Our objective in this chapter is to further our understanding of market liquidity by looking at market structures. Our treatment investigates sev-eral distinct but related building blocks, such as the role of institutional arrangements, the role of technology, and the importance of capital. We explore questions such as how institutional arrangements and features of a particular structure either enhance or inhibit market liquidity, and how new capital requirements are changing the process of financial intermediation.1

Market microstructure insights into security price formation

In an efficient market, security prices fully reflect all available infor-mation,2 and will accordingly change only in response to the arrival of new information.3 Market efficiency is a powerful concept of finance theory, it represents a market in which “prices provide accurate signals for resource allocation that is a market in which firms can make pro-duction-investment decisions, and investors can choose among securi-ties that represent ownership of firms’ activities under the assumption that security prices at any time ‘fully reflect’ all available information.”4 This hypothesis convinces us of the usefulness of security prices, but it does not explain the process of how information is being incorporated into security prices.

The Walrasian auctioneer provides the simplest characterization of price formation. According to this framework, a fictitious auctioneer


orchestrates the price formation process in a setup without any time constraints. Traders submit and revise their orders until the auctioneer finds a market price that allows all purchase and sale orders to clear. This market-clearing price is the equilibrium price or fundamental value of the security.

Economist Harold Demsetz5 expanded the role of the single Walrasian auctioneer to several market makers and explained how the need for immediacy can affect price formation. Demsetz argued that, while a trader willing to wait might trade at the single price envisioned in the Walrasian framework, traders not wanting to wait have to pay a price for immediacy. The price for immediacy comes in the form of compensa-tion to the market maker, who has to stand ready to execute trades upon receipt of an order.6 This results in two equilibrium prices, the bid price, at which an immediate sale can be executed, and the ask price, at which an immediate purchase can be executed. The size of the spread between the bid and ask prices, which is often used as a measure of market liquid-ity, depends, among other factors, on the structure of the market and on the number of participants competing for orders. A case in point is the reduction in bid-ask spreads on the National Association of Securities Dealers Automated Quotations (NASDAQ) following the introduction by the US Securities and Exchange Commission (SEC) of two regulatory changes in 1997. The limit order display rule forced NASDAQ dealers to execute or display any customer’s limit orders better than their own, and the “quote rule” required dealers trading in multiple venues to make their best quotes available to the public.

Demsetz’s replacement of the Walrasian auctioneer with market mark-ers also introduced the role of inventory in price formation. The market maker or price-setting agent uses prices to balance supply and demand across time. Order flow is uncertain, which exposes market makers to inventory risk because they may not necessarily be able to unwind a posi-tion immediately. As the market maker accumulates a long position in a security, he is exposed to the risk of a price drop, while a short position exposes him to the risk of a price increase. If a market maker is risk averse, his quotes should depend directly on his exposure to risky inventory. The size of the bid-ask spread should, in principle, be related to inventory


holding costs. Empirical evidence does not support inventory effects in financial markets.7

An alternative framework to price formation looks at information revealed by order flow. If some traders have superior information about the underlying value of a security, their trades can reveal information about the underlying value and so affect the behavior of prices.

This “learning problem” has been explained by Easley and O’Hara, two esteemed professors from Cornell University, as a Bayesian learn-ing process.8 In the Easley and O’Hara setup, potential buyers and sellers trade with market makers. Market makers are competing with each other for order flow and must set prices at which they will buy or sell any quantity of a security. The Easley and O’Hara setup adopts the actors proposed by Albert Kyle: informed traders, the market makers, and uninformed noise traders who transact randomly.9 Noise traders camouflage the activities of informed traders, whose transactions are organized in such a way that their private information is reflected grad-ually in market prices.10

Market makers have a prior belief about the true value of a security. Traders ask competing market makers for their price-quantity quotes. The trader then either does not trade, takes the best quote for the quantity he wants to trade if he is uninformed, or takes the profit-maximizing quote if he is informed. Informed traders observe some data and update their expected value of the security to be either higher or lower than the market makers’ prior belief of the security value. The revealed information influ-ences the informed trader’s desired trading quantity, with good news caus-ing the trader to buy and bad news eliciting sell orders. Assuming that uninformed traders are equally likely to buy or sell, whatever the informa-tion might be, the market makers then update their quotes conditional on the type of trade. The posterior then becomes the new prior, more data are observed, and the updating process continues. In information-based mod-els, the solution to this learning problem determines the prices set by mar-ket makers. The ask price equals the expected value of the security given that a trader wishes to buy. The bid price is defined similarly given that a trader wishes to sell. An important characteristic of these prices is that they explicitly depend on the probability of a sale or a purchase. Good news


will result in an excess of buy orders as informed traders decide to buy. Likewise, bad news will result in an excess of sell orders as informed trad-ers decide to sell. Information-based models explain the bid-ask spread and provide insight into how the price change relates to order flow.

In “perfect” and complete markets, with comprehensive information available to all market participants, securities can be traded at their fun-damental value. Markets are not perfect. Depending on the institutional arrangements of a trade, there are information asymmetries whereby borrowers (issuers of securities) know more about the risks than lend-ers (or buyers of securities). So market participants may be reluctant to trade in those securities whose characteristics and behavior under chang-ing economic conditions are not well known. For example, the price of Apple stock is much easier to discover than the price of tranche B of the asset-backed security (ABS) issued by shelf X. In times of distress, when uncertainty increases, it may be impossible to determine the true value of securities in more opaque markets, due to a lack of trading. Market liquid-ity is inversely related to the degree of information asymmetry prevailing among economic agents. This phenomenon rests on the observations of 1970 Nobel laureate George Akerlof, who proposed that it is the diffi-culty of distinguishing good quality from bad quality that is the inherent problem. As explained in Akerlof ’s celebrated theory on the market for bad used cars, or “lemons,” a market may altogether disappear (the most extreme form of illiquidity) if information is sufficiently asymmetric.11

Given that some traders have superior information, prices along the adjustment path may not fully reflect all the information from public and private sources (i.e., be strong-form efficient) and there can be great dif-ferences in the speed with which prices move toward full information. But prices ultimately do converge to their true, full-information value, so markets are strong-form efficient in the limit.

Overview of structural features of market design and their effect on market liquidity

How can we assess whether a particular market structure enhances market liquidity? As a general matter, a market is considered efficient in fulfilling


its role of providing liquidity if it incorporates new information quickly and accurately into prices. The degree of information asymmetry between suppliers and demanders of liquidity determine market liquidity.12 But how do we quantify this further? A working definition often used in mar-ket microstructure discussions identifies the features of a liquid market as tightness of bid-ask spread, depth, and resiliency. Tightness of bid-ask spread measures the cost of a reversal of a position at short notice. Market depth indicates the volume of transactions that may be immediately exe-cuted without introducing slippage to the price, and resiliency refers to the speed with which prices revert to their equilibrium level following a random shock to transaction flow. Market depth and resiliency indicate the market’s ability to absorb significant volumes without adverse effects on prices.

Market structures necessarily involve a trade-off between these various dimensions. For example, greater competition among institutions provid-ing market-making services can reduce the bid-ask spread and thereby improve tightness. But lower profitability for market making can lead to a withdrawal of capital and a reduction in the size of transactions that can be absorbed (reduce market depth).

Liquidity depends on how the market is structured, but in turn liquid-ity needs also dictate the structure of the market. We next review some of the critical structural issues in the creation of liquidity.

Adverse selection and asymmetric information

To understand the link between liquidity and adverse selection, we again look at the theory of market microstructure for insight. Professors Lawrence Glosten and Paul Milgrom created a well-accepted model that showed that market makers in a competitive environment widen the bid-ask spread beyond what it would otherwise be to recover from uninformed traders what they lose to informed traders.13 This so-called adverse selec-tion component induces a wider bid-ask spread to compensate market makers for the possibility of asymmetric information.14

Market liquidity is likely to be enhanced if information about the assets’ value is distributed symmetrically between intermediaries and potential


buyers and sellers. Wide bid-ask spreads set by intermediaries can often be interpreted as reflecting asymmetric information.

How does adverse selection affect market structure? To illustrate this link, consider the current market setting in Europe where, unlike in the United States, the regulator does not explicitly prohibit trades to execute at prices that are inferior to the best available price across all relevant trading platforms.15 Examples of multimarket trading are French and German stock listed on their respective home markets (Deutsche Börse and Euronext) and on Chi-X, the first multilateral trading facility (MTF). For these stocks, liquidity providers post quotes in two separate platforms, the Deutsche Börse and Euronext, referred to as the primary market and the lower-cost Chi-X, an MTF. While the primary market is accessible by all agents, trading on the MTF requires a so-called smart order routing system that is available to a subset of agents. Empirical results show that the “smart routers” are also more informed, so trades on the MTF carry significant more private information.16 This implies that liquidity provid-ers on the MTF incur a higher adverse selection risk precisely because an important fraction of the uninformed order flow is held captive in the primary markets. Primary markets display a better quote than the MTF Chi-X’s, or, put differently, primary markets’ best bid-price will improve or match the bid on Chi-X’s due to the excess adverse selection risk on the MTF.

The example illustrates how the competitiveness of alternative trading platforms is hampered by the concentration of uninformed order flow in primary markets.

Transparency

Transparency captures the amount of information about the trading pro-cess that is available to market participants. Information in this context can be prices, quotes, volumes, order flow, and the identity of market par-ticipants. Consider a retail investor interested in buying a share of Apple for his investment account.17 He could check the latest prices available on Yahoo Finance, but these are delayed, so uncertainty about the exact price he will get at execution remains. Alternatively, the investor could


subscribe to a real-time feed such as ICAP or Reuters and learn the price of the most recent trades. But such subscriptions are costly and do not provide quotes for the next trade. What if the investor wants to buy a cor-porate bond or a structured product? Most corporate bonds and struc-tured products are traded in the OTC market, which is at the opaque end of the spectrum—no information on quotes from market makers is made available. The lack of transparency affects not only retail investors but also mutual fund managers or brokers, who often do not have a com-plete picture of the market. A critical component of price discovery and a hot-button regulatory issue is for market participants to have access to sufficient information about market conditions.

It is useful to divide transparency into two dimensions. Pretrade trans-parency relates to the quantity and quality of information on the trading process that is made available to market participants. Pretrade informa-tion such as bid and ask quotations, trade prices, order flow, and volumes is useful to traders, who may use these in the development of trading strategies and to improve their estimates of a security’s value. Posttrade transparency relates to the extent to which posttrade information such as execution time, volume, and price is disseminated among brokers, cus-tomers, and the public and to the speed of dissemination in real time versus delayed feed.

A reason for the crucial role of transparency in trading is because it affects how trading gains are distributed between the investors and the market makers. Delays in reporting usually favor the market makers and potentially also large traders, while transparency favors the other market participants and the markets as a whole.

Transparency about quotes, orders, and traders’ identities gener-ally improves market liquidity for uninformed traders. Many exchanges implement electronic limit order books that ensure a level playing field for liquidity suppliers in order to mitigate market power. In 2002, the New York Stock Exchange (NYSE) started disseminating electronically its limit order book, which increased transparency and enabled investors to monitor and cancel their limit orders. Greater transparency of the open limit order book reduced the informational advantage of the specialist and attracted more limit orders in the book. This initiative increased


the liquidity displayed on the NYSE.18 In contrast, if the dealer market becomes too transparent, dealers may lose their incentive to intermediate given their fixed costs and the risk of adverse selection by informed trad-ers. A sufficiently large increase in transparency in the OTC market could potentially reduce trading opportunities for investors.19

Transparency depends not only on the availability but also on the cost of information. The degree of transparency also varies by security type. Realizing the positive feedback between greater transparency and greater market liquidity, the Financial Industry Regulatory Authority (FINRA) developed the Trade Reporting and Compliance Engine, or “TRACE,” in 2004. TRACE is an automated system that, among other things, accommodates reporting and the dissemination of transaction reports. Execution costs in the municipal bond markets fell by half after posttrade information was disseminated through TRACE.

Liquidity profile of securities

Each financial security has a unique “liquidity profile” that determines the ease with which its salient features can be credibly communicated to a large investor base. The more homogeneous or standardized the secu-rity, the more likely multiple buyers and sellers will be found, which will greatly increase trading volumes and thus market liquidity.

For instance, futures contracts are standardized across various features of the underlying asset or commodity in order to attract heterogeneous buyers and sellers. The maturity date, a par or notional amount, a speci-fied deliverable item with transparent characteristics, and an established trading unit, or “tick size,” are all relevant standard features of such a contract. At the other end of the spectrum are bespoke securities traded in the OTC market, which is designed specifically to suit the buyer and seller in a way that personalizes the transaction to the investment pro-file or hedging needs of the participant. Examples include securitized products such as ABS, collateralized debt obligations, and exotic deriva-tives such as variance swaps. These products are often not intended to be traded in a broader market, but are meant to be held until maturity by the original buyer.


Institutional arrangements play an important role in determining the liquidity profile of securities. Often, the characteristics of an asset that influence the degree of liquidity are determined before the first trade in a security takes place. Standardization is also a critical consideration in the debate on central clearing mandates. Standardization of product features is a precursor for exchange-like execution, central clearing, and greater transparency of OTC markets.

Standardization increases the notional turnover, which drives down the trading cost for the entire asset class. For example, Figure 3.1 shows the estimated average cost per trade versus the average number of daily trades for a spectrum of securities.20 The empirical results shown in Figure 3.1 clearly indicate the much greater trading volumes for com-moditized products such as cash equities or listed derivatives when com-pared with bespoke products in the OTC market.

It is also important to distinguish between primary and secondary market liquidity because high volumes in primary markets do not neces-sarily imply liquidity in the secondary market. Particularly, the markets

Cash-Equities

Listed Derivatives

FX-Spot

Cash-RatesOTCD-FX

OTCD–RatesCDS

OTCD-CommodityOTCD-Equity

0.1

1

10

100

1000

1 10 100 1000 10000 100000

Est

imat

ed A

vera

ge C

ost P

er T

rade

($)

Average Number of Daily Trades (000’s)

VolumeIncrease

= 5x

Cost Reduction

Figure 3.1 Volume versus cost per trade by security class (OTCD refers to OTC derivatives).

Sources: ICAP, BIS, WFE, TABB Group.


for customized credit derivatives and collateralized debt obligations are highly tailored to meet specific investor needs, which make them rather illiquid in the secondary market. The lack of secondary market liquid-ity may not be a major problem if the users, often long-term investors, desire the credit exposure and do not engage in active trading. However, an investor wishing to unwind or modify the position may have to rely on the initial arranger of the transaction, who may not be willing or able to provide liquidity under stressed market conditions, or may do so only at significant depressed prices.

Market structures

Market structures are very diverse and constantly evolving due to regu-latory reform and technological advances. An infinite number of rules and trading protocols determine the actions of market participants, for example, how to place an order, how much information is available about other participants’ actions (actions can include either all or any of the participants’ quotes, order flow, or transaction prices), what is the pro-tocol for matching buy and sell orders, and whether the execution price differs depending on who placed the order. Institutional arrangements play a critical role in the design of markets and ultimately in market liquidity. We illustrate this by an in-depth look at representative market structures.

On the one end of the spectrum we have the “quote-driven” dealer market, as is typical of trading in the OTC market. Corporate bonds in the United States and structured securities such as collateralized mortgage obligations and residential mortgage obligations, emerging market debt, currencies, some derivatives, and certain equities are traded in the OTC markets. Final investors can only trade at the bid and ask quotes posted by specialized intermediaries, also called dealers or market makers.

On the other end of the spectrum we have the “order-driven” auction, or limit order market, as is typical of trading on centralized exchanges. Derivatives such as Eurodollar futures, options on Eurodollars, and most equities are traded via electronic limit order books on centralized mar-kets such as the NYSE (NYSE-Euronext), London Stock Exchange (LSE),


Deutsche Börse, LIFFE, CME Group, and so forth. An exchange typi-cally runs a limit order market, in which final investors interact directly. Buy and sell orders are consolidated in a central order book and executed according to time and price priority. The highest buy orders and the cheapest sell orders are more likely to be executed.

The NYSE priority rules determine the coordination of prices in these trading mechanisms. The NASDAQ on the other hand is a strictly elec-tronic exchange in which large investment companies buy and sell securi-ties through an electronic network. Each market maker on the NASDAQ is required to give a two-sided quote, meaning they must state a firm bid price and a firm ask price that they are willing to honor.

In the 1970s, most shares were listed on the NYSE, and all exchanges were floor-based auction markets. Nonlisted securities were traded in an informal dealer market, which became a formalized dealer market known as NASDAQ. It consisted entirely of dealers who made markets in securities that were not listed on any exchange by offering to buy or sell shares as principals. Innovation and the SEC’s adoption of the Regulation Alternative Trading System (Regulation ATS) in 1998 for-malized the development and registration of new trading systems.21 Key elements of the Regulation ATS were the registration of for-profit enti-ties as exchanges, providing ATSs the option to register as exchanges and expanding the definition of exchange to include ATSs that perform market functions. The implications of these regulatory changes were far reaching—exchanges became for-profit entities, which basically put them in direct competition with each other for market order flow. Entities act-ing as electronic communication networks (ECNs), which are ATSs that make their quotes available to the public, could register as exchanges under the new regulations. These ECNs were fully electronic, and there-fore competed for the same order flow as the NASDAQ, the only incum-bent electronic exchange at the time. Over time, dealers were replaced by order-matching computer systems. So fierce is the competition among trading venues that NASDAQ trades less than 25 percent of NASDAQ-listed shares, and most of the rest are traded on the ECNs.22 Historically, the NYSE was the dominant trading venue for NYSE-listed securities, with almost 80 percent of all trading in NYSE-listed stocks taking place


on the NYSE. Due to regulatory changes and technological advancement, however, less than 30 percent of NYSE-listed stocks traded on the NYSE during November 2014.

The SEC’s regulatory decisions, in particular the trade-through rule, are an important factor in these developments. The trade-through rule requires that orders to buy or sell securities listed on any registered stock exchange be sent for execution to the market in which the best price is posted. Assume that an institutional buyer wants to purchase shares of an NYSE-listed security, which has been posted for sale on an ECN at $30. At the same time, an offer to sell 100 shares of the security at $29.50 is posted on the floor of the NYSE. Under the trade-through rule, the order must be sent to the NYSE to clear. Because the NYSE is the largest and most liquid market for these securities, its prices tend to be the best, and most orders flow there first. Thus, the trade-through rule has reduced the likelihood that any serious competition for the NYSE will arise, thereby ensuring the dominant position of the NYSE in trading NYSE-listed securities.

Since NASDAQ was a dealer market, it was not subject to the trade-through rule and was thus vulnerable to competition from ECNs that offered superior services or other advantages. ECNs offered superior trading over the NASDAQ dealer market, which particularly benefited institutional investors, who could execute trades at lower cost and achieve better overall pricing on the ECNs. Eventually, in a bid to remain a viable market, NASDAQ sought the approval of the SEC to become a privately owned electronic market, enabling them to compete with the ECNs for market share in NASDAQ securities.

The SEC’s adoption Regulation National Market System (NMS) in June 2005 provided the regulatory framework that governs the current shape of the US equity markets.23 The SEC designed Regulation NMS to establish extensive baseline rules to govern how trading centers and broker-dealers interact with the rest of the market when routing orders for execution, displaying quotations, and disseminating market data to the public.24 Regulation NMS proposed to apply the trade-through rule to the NASDAQ market, to which it had not been applicable before, but it also allowed institutional investors to opt out of the trade-through rule on a trade-by-trade basis. Regulation NMS increased competition between


exchanges with a growing number of equity exchanges and other ven-ues. The volume of shares of stocks trading on the NYSE decreased from 78 percent to less than 30 percent between 2004 and 2014. As of the third quarter of 2014, other venues, particularly NASDAQ and some ECNs, executed more than 70 percent of volume in NYSE stocks.25

In our discussion of liquidity within representative market structures in the next sections, we revisit some familiar themes of aggregation of buyers and sellers, speed of transacting, information sharing and so on. For example, the speed of transacting is greatly influenced by the exis-tence or absence of an intermediary and the trading venue. The congrega-tion of buyers and sellers, either physically or electronically, determines the ease of trading—it is much easier to match buyers and sellers in an exchange-traded environment, with well-established methods of record-ing and publishing prices, than in the OTC market (an exception is for-eign exchange markets, in which OTC spot, forward, and option trades exceed their exchange-traded equivalents).

Each of these market structures poses a unique set of challenges to policy development. Different causes of market illiquidity call for differ-ent remedies. If order processing costs are high, upgrading technology or rules that would open competition among trading platforms may be appropriate, but if adverse selection causes illiquidity, actions against insider trading and timely release of trade-related market information to all participants may mitigate the advantage of informed traders.

Key features of the over-the-counter/dealer market

We find a clear distinction between participants who supply liquidity and participants who demand liquidity in the dealer market. In a typi-cal setting, dealers supply liquidity to investors who demand liquidity. Investors request quotes from a dealer or multiple dealers, typically over the phone or using an electronic access network. Dealers quote bid prices to customers interested in selling securities and similarly quote ask prices to customers wanting to buy securities. In the corporate bond markets and in some foreign exchange markets, dealers provide quotes only upon request. However, in other markets such as the quote-driven NASDAQ


Stock Market, dealers must continuously post firm prices at which they will trade. In both cases, dealer profit depends on the volume of trade it handles and on the difference between the bid price and the ask price. Dealers must attract order flow, which motivates them to quote aggres-sive prices as trades are executed only with the dealer who provides most competitive bid or ask. The dealer typically acts as the counterparty to all customer trades. No price priority is enforced.

The dealer market is considered bilateral. A transaction is privately negotiated between two counterparties involved in the final transaction—the investor and the market maker with the most competitive quote are privy to the terms of the trade. For this reason, the OTC market is con-sidered opaque. Many dealer markets, such as the US corporate bond market, do not offer any real-time information.26 In the currency mar-ket, Bloomberg’s and Reuters’ screens provide information on indicative quotes, but dealers are not committed to execute trades at those prices.

Dealers execute trades with final investors, but they can also trade directly with each other in the interdealer market. Some dealers also act as brokers, an execution agent who routes buy and sell orders between the final investors and other dealers. Another segment of brokerage is the interdealer-broker, who is a specialized execution agent for trades in the interdealer market. Dealers such as Goldman Sachs are referred to as broker-dealers because they offer both brokerage and market-making ser-vices. Brokers and dealers are collectively referred to as the “sell side” of the securities industry because they facilitate trade execution in the market. Final investors, which could be households, intuitional investors, firms, and governments, are referred to as the “buy side.”

Search and bargaining in OTC markets

The OTC market has two distinct features: bilateral trade that is privately negotiated between market makers and their customers, and the respon-sibility of market making resting solely with dealers. These features sig-nify several interesting features of market liquidity.

An important role of dealers is to “discover” the price that will produce a two-way order flow. In order to fulfill their role as liquidity provides,


dealers typically have inventories of the securities they trade that expose them to the risk of a change in the prices of securities and hence a potential loss on the value of their inventories. A dealer could reduce his exposure to market risks by hedging these positions, using an offsetting customer transaction or by transacting with another dealer. Some past academic studies consider the management of inventory and inventory risk to be a major determinant of the bid-ask spread in the dealer market.27

More recent academic studies consider search cost in locating coun-terparties and the bargaining power of dealers and investors to be critical components of market liquidity.28 The absence of a centralized market implies that an investor who wants to buy must search for a seller, incur-ring opportunity or other cost until he finds one. Similarly, an inves-tor who wants to sell a security must find a counterparty who is willing to sell the desired security and the desired quantity. Search costs are partly attributed to institutional arrangements in the dealer market. For example, OTC derivatives are typically executed between a dealer and the final investor, only after the parties have signed a master agreement that conforms to standards set by the International Swap and Derivative Association (ISDA). The master agreement defines, among other things, the collateral requirements as well as the obligations of the two counter-parties in the event that one of them defaults. After the initial signature, the posted collateral may be adjusted periodically to reflect changes in the market values of the derivative contracts between the counterparties. Delays in finding a counterparty could arise because of delays needed to verify credit standing, or to arrange trade authorization and financing or the time necessary to familiarize investors with contractual terms and product type.29 In some cases investors negotiate price concessions if they need to trade quickly, but this typically lead to noncompetitive execution prices. In general, the search process is time consuming, which increases the speed with which transactions can be executed. The search process is also costly, which manifests as financing costs or opportunity costs.

Once a counterparty is located, the price is bilaterally negotiated. The dealer typically sets the bid-ask spread based on the customer’s per-ceived outside options of finding a different counterparty for the trade. According to a study by Professor Darrell Duffie et al., the execution price


reflects the participants’ outside option to find another counterparty who can facilitate immediate execution.30 The feasibility of outside options from the investor’s perspective implies that they may be able to buy the same security from a different dealer at a lower price. Similarly, from the dealer’s perspective it implies that the dealer may be able to sell the same security to a different investor at a higher price. Smaller investors often have an account with only one or a few dealers, which gives them less bargaining power than larger investors, such as institutional fund managers, who have access to multiple dealers at any given point and who can typically trade with multiple dealers. Consider the US market for municipal bonds as an example. The large amounts and high frequency of trade by institutional investors relative to retail investors gives them greater bargaining power, which is evident in the more competitive execution prices of institutional investors.

Bargaining power also introduces a trade-off between execution speed and price. An investor with a large order can ask a dealer to quote a price at which he is willing to execute the whole order. This price guarantees immediate execution of the full order, but it may be suboptimal to the price the trader would obtain by splitting the order among several dealers over time.31

Market liquidity also depends on the presence of a sufficient number of counterparties and their willingness to trade. The latter depends on investors’ expectations regarding price developments and also their risk aversion at a given time, as well as the information available (e.g., on issu-ers’ creditworthiness). A “good equilibrium” of regular liquidity there-fore presupposes heterogeneous expectations and behavior, ensuring the execution of orders irrespective of the transaction direction. It then only takes a hint of doubt creeping into market operators’ minds to radically change the market configuration and trigger a liquidity crisis.

The structural features of the OTC market are the bilateral negotiation of trade and the separation of agents who supply and demand liquidity. The size of the bid-ask spread and market liquidity more generally are determined by search costs, inventory costs, outside options, and bar-gaining power. Market participants require compensation for bearing the costs and the uncertainties of dealer market execution. These risks should therefore affect security prices as we discuss in more detail in chapter 4.


The structure of the OTC market place a central role in the distri-bution of trading gains between the buy-side investors and the sell-side intermediaries, who can have very different views on how trading should be organized.

Key features of limit order books

The nature of limit order markets fundamentally differs from dealer mar-kets in that orders are executed in a centralized market in which the buy and sell orders of participants are matched directly. The centralized mar-ketplace can be the floor of an exchange or a virtual, electronic trading platform. Any participant can either supply or demand liquidity.

Execution prices are determined using sophisticated auction pro-cedures based on the time and price priority of orders. Price priority dictates that the highest price buy order and the lowest price sell order take precedence in execution. Time priority dictates a “first in first out” sequencing whereby older limit orders are executed before more recent orders. First movers at a given price are therefore rewarded for providing liquidity at that price.

Participants execute trades by submitting limit orders, market orders, or a combination of both. The key difference between these two types of orders is the probability of execution and the price at which each is to be executed. The most common type of limit order specifies the price and a given amount of a security to be transacted. A buy limit order speci-fies the maximum price at which the trader is prepared to buy a stated amount of the security, while a sell limit order is the minimum price the seller will accept for a given amount. Since the specific prices are either above the current ask price or below the current bid price, limit orders are stored in the order book, and a movement in prices is required for such orders to become active. Limit orders provide liquidity to the mar-ket, and it is possible for the trader to improve the prices in the market.32 However, limit orders face the risk of nonexecution—that is, unexecuted limit orders queue up in a limit order book and are in force until filled or cancelled. Limit orders also face adverse selection risk. If new pub-lic information arrives, limit orders can become mispriced and may be executed at a loss.


A market order only specifies the amount of the security and not the execution price. Assuming there is an outstanding limit order on the other side, a market order is immediately executed at the best price cur-rently available in the market. The limit price and the size of the order book determine the market-clearing price, which implies that the same security can be traded at different prices. As explained by Bruno Biais, Larry Glosten, and Chester Spatt, authors of Market Microstructure, in an article published in the Journal of Financial Markets, “Another differ-ence between call auctions and continuous trading is that in the former all trades are executed at a single uniform price, while in the latter, as orders walk up or down the book, and as the latter evolves, trades are filled at different prices.”33 A key strategic decision for participants in this market is the choice between submitting a limit order versus a market order because it determines both the speed of trading and the execution price. Terry Foucault suggests that volatility of the asset is an important determinant of the mix between market and limit orders.34 In a volatile market, the probability of mispricing an asset is higher, and so limit order traders require greater compensation for bearing mispricing risk. Limit orders quote relatively wide bid-ask spreads, which raises the cost of market order trading. One could argue that an increase in price volatility increases the relative proportion of limit orders.

Electronic limit order books enable global participation in the order book. It is also feasible to implement complex algorithms that predefine priority rules using computers. Indeed, in recent years, there has been a general move toward open electronic limit order books. Electronic trading is rapidly replacing lively floor trading. In 2007, approximately 95 percent of trades and 85 percent of volume at the NYSE were handled by computer programs.

Most equities and some derivatives are either pure electronic limit order markets or at least allow for customer limit order markets in addi-tion to on-exchange market making. For example, Bank of America was one of the most actively traded equities on the NYSE during the last few months of 2014. Trading occurred on both the traditional NYSE cus-tomer limit order market and the NYSE Arca all-electronic exchange, with on average 8 percent of daily volume traded on the NYSE Arca.


Another example is Facebook, which is listed on the NASDAQ but also on the NYSE Arca all-electronic exchange. During the last few months of 2014, an average of 9 percent of the daily volume of Facebook was traded on the NYSE Arca. The CME Group’s CME Globex platform is an open access marketplace that allows customers to participate directly in the trading process, view the book of orders and prices, and enter their own. CME Globex offers global access to all major asset classes—interest rates, equity indexes, FX, agriculture, energy, metals, weather, and real estate.35 Globex accounted for approximately 90 percent of the exchange’s aver-age daily volume of contracts traded in 2013.

We expect this market model to develop further, which is consistent with the view that the electronic open limit order book is inevitable. Rather than a gigantic integrated order book, it is likely that several limit order books will coexist. Such coexistence is desirable, since, along with the competition among liquidity suppliers within one market, the com-petition across markets plays an important role in curbing market power and intermediation rents. Several exchanges have recently gone public, for example, Euronext and the London and Frankfurt Bourses. In con-trast, the NYSE is not publicly held, but rather is owned by its members (specialists and brokers).

Because of trading on a centralized market, illiquidity costs due to search and bargaining power do not affect trading, but other issues such as trade-offs between the cost and benefits of limit orders versus market orders are important determinants of market liquidity.

Liquidity in limit order books

Liquidity on an exchange depends critically on having a sufficient number of balanced buy and sell orders—limit orders only execute if enough mar-ket orders arrive in the future to execute them plus all the additional limit orders already in the queue. In his classic paper, the economist Harold Demsetz argued that the cost of transacting declines with an increase in trading activity for that security.36 The order in the front of the limit order queue will be filled more quickly if the security is more active. In general, traders will be willing to raise the price if they are willing to buy and lower the price if they are willing to sell in order to get to the front of the


queue. This dynamic effectively increases the bid-ask spread. However, if the security is active, in other words, if the frequency of transacting is high, then the cost of waiting in the trading queue will be lower. The bid-ask spread will be smaller because traders do not need to adjust their bid and ask prices to preempt positions in the trading queue. In equilibrium, longer waiting times for inactive securities translate into less market liquidity. A related aspect of limit order market liquidity is measured as the probability that enough limit orders will arrive to return the book to the minimum bid-ask spread before the next transaction. This is referred to as the resiliency of the order book. The behavior of traders in limit order books is further explained as follows: both patient and impatient investors use market orders when the spread is at its minimum, but only impatient investors use market order when the spread is wider.

The importance of sufficient volume becomes clear in the extreme case in which the limit order book is too thin for price discovery to hap-pen. A limit order market can fail, even in the absence of the adverse selection problems that plaque the OTC market.37 The intuition for market failure is that if the limit order book is too thin, then price elas-tic market order submitters will scale back their market order submis-sions. However, as the endogenous distribution of submitted market order quantities shifts toward zero, the probability of limit order execu-tion falls, which, given ex-ante limit order submission costs, leads to fewer limit orders and, thus, a thinner book. If market order submis-sions are sufficiently elastic, the limit order book may fail. Portniaguina et al. showed that the tick size is an important institutional feature that is relevant for preventing market failure. If the tick size is too small, it is easier for the specialist to undercut the book which, in equilibrium, makes the book thinner.

The conceptual appeal of limit orders masks the complexity of the dynamic interactions and nonlinear payoffs that can be generated. A limit order book does not have a unique, market-wide clearing price, but rather a sequence of matched pairs of buy and sell orders over time. Prices are formed as investors arrive and trade asynchronously. This market structure is fundamentally different from the Walrasian market, in which the execution price reflects the clearing price of aggregate supply and


demand. The prevalence of limit order markets validates the theoretical results of Glosten that limit order markets provide the maximal liquidity in the presence of adverse selection.38 The prevalence of order flow from high-frequency traders in recent years, discussed in more detail toward the end of this chapter, raises important questions about the optimality of limit order markets.39

Institutional features such as tick size, trading volume, and the dynam-ics of order flow are important market design considerations in a limit order market that should be considered in order to improve market liquidity and price discovery. Empirical results show that exogenous costs such as order processing costs account for over 80 percent of the bid-ask spread in limit order markets.40 Unlike asymmetric information costs, which depend on information revealed by the total trade of active inves-tors across all markets, order submission costs are independent of what happens in other markets.41

Market liquidity: NYSE versus NASDAQ trading

The structure of the market has an effect on liquidity. We find empirical evidence of the effect of structure on liquidity by comparing the bid-ask spread of transactions executed on the NYSE/AMSE auction mar-ket and transactions executed on the NASDAQ dealer market. Recall that traders who demand immediate execution, at the current bid price or current ask price, place market orders. The difference between the ask price and the bid price, or the bid-ask spread, measures the price concession paid for immediacy that was formalized in a 1968 study by economist Harold Demsetz.42 In the spirit of Demsetz, Professor Hans Stoll proposed the following cross-sectional regression model of bid-ask spread:43

s a a V a a MV a P a No= + + + + + +1 22

3 4 5log log log log ,σ ε

where s is proportional to the quoted half-spread defined as ½ (ask price-bid price)/P, V is the dollar daily volume, N is the number of trades per day, σ2 is the stock’s return variance, MV is the stock’s market value, P is the stock’s closing price, and ε is an error term.


The rationale for these variables is the drivers of market liquidity in order books. Increases in volume, number of trades, and firm size raise the probability of locating a counterparty, thereby increasing the possibil-ity of immediate execution. The stock’s return variable measures the risk of adverse price change that affects the supplier of liquidity, as discussed before. Price controls for the effect of discreteness is an additional proxy for risk in that low price stocks tend to be riskier.

The quoted spreads are negatively related to measures of trading activ-ity, such as volume. Spreads are also negatively related to stock price and positively related to a stock’s volatility. There are some differences by exchange, but regardless of trading on the NASDAQ or NYSE, the liquid-ity measures are statistically significant measures of the quoted bid-ask spread. Stoll’s study found that liquidity is lower on the NASDAQ than on the NYSE.

Dark pools

A dark pool refers narrowly to an Alternative Trading System (ATS) that does not display bids and offers in the public quotation stream. More broadly, a dark pool refers to sources of liquidity that are not reflected in public quotes, such as dark orders on exchanges and internalization of orders by brokers and dealers. According to 2008 estimates, the vol-ume percentage of dark pools has remained at approximately 20.44 While the organizational structure of dark pools is relatively new, the function of offering shielded liquidity is not new. Trading in dark pools has been around for as long as there has been a market. Floor traders on the NYSE, for example, represent a manual market that can only be accessed by sending a buy or sell order to the floor. Dark pools provide a mechanism for large institutional investors who need to trade in substantial size with-out displaying their trading interest. Many dark ATSs exist as an attempt to service the trading needs of different types of investors and traders.

An important regulatory aspect and a source of the increased cost of trading is the fragmentation of dark pools.45 As of the end of 2013, dark pools were really a disjointed network of individual alternative trading systems, each operated according to its own rules for price discovery


and trading. We can categorize dark ATS into three groups based on how traded prices are formed. Systems such as IGT Posit, Liquinet, and Instinet are classified as group one pools. Owners of group one pools act as agents for matching customer orders (as opposed to trading for their own accounts), and orders are executed at volume-weighted aver-age prices from the lit markets. The second group operates dark pools as continuous nondisplayed limit order books, accepting both limit and market orders. Group two includes pools owned by major broker-dealers, including Credit Suisse Crossfinder, Goldman Sachs Sigma X, Citi Match, Barclays LX, Morgan Stanley MS Pool, and UBS PIN. Unlike group one pools, group two pools derive their own execution prices from the limited prices of submitted prices. The third group includes high-speed systems such as Getco and Knight that act like fast electronic market makers that immediately accept or reject incoming orders. Participants in groups one and two usually act as trading agents to their customers. In contrast, par-ticipants in group three do not act as agents for other people but trade as principals for their own accounts.46

This large number of separately organized dark pools poses challenges for market participants. These include the basic logistical task and cost of establishing connectivity to many different venues. The only way to know whether a dark pool is liquid, is to route an order to the pool. Routing this type of pinging order is a less efficient means of assessing liquidity than viewing centrally displayed quotes from multiple trading venues. A key cost of fragmentation for traders is the opportunity cost of being out of the market on one venue when searching for liquidity in other venues. Competitive forces seem particularly apt to address the problem of frag-mented dark pools. The ultimate users of dark pools seem likely to pres-sure operators of the less successful pools to consolidate with other pools.

High-frequency trading

High-frequency trading broadly refers to automated trading that employs technology and algorithms to capitalize on very short-lived informa-tion gleaned from publicly available data using sophisticated statisti-cal, machine learning and other quantitative techniques.47 High-speed


trading had been technologically possible for many years, but it was leg-islative changes such as the US Regulation National Market System Law of 2005, known as “Reg NMS,” and the European Markets in Financial Instruments Directive, or “MiFID,” in force since November 2007 that enabled actual trading. The SEC designed the ITS order routing system to connect exchanges to the National Market System and to each other, which offered remote access to market participants. These changes in the market structure, combined with innovative financial strategies, paved the way for a new era of profitable high-frequency trading. One of the most nota-ble rules under Reg NMS is Rule 611, referred to as the “trade-through rule,” which was originally released by the SEC as the “order protection rule.” The objective of the rule was to ensure that investors’ orders are being executed at the best available price. A trade-through occurs when one trading center executes an order at a price that is inferior to the price of a protected quotation displayed at another trading center.48 The impli-cations of this rule were that floor-based trading systems lost their pri-macy to electronic systems. In February 2015, the CME Group approved the closing of their historical trading pits in Chicago and New York.49 High-frequency trading is now the norm for trading financial assets in electronic markets around the world—equities, foreign exchange, futures, or commodities. High-frequency traders provide not only the bulk of the volume in these markets but also most of the liquidity.50

Many firms that engage in high-frequency trading seek to end the day with little or no exposure to the market. While the speed of information flows and order flows is critical to high-frequency trading firms, a view of high-frequency trading as a faster version of the same old markets is too narrow. High-frequency trading represents a new paradigm for trad-ing financial assets based on “event-based time” (such as transactions or volume) rather than chronological time, reflecting the fact that machines operate not on a time basis but rather on a volume basis. For example, a high-frequency trader will monetize accurate forecasts of e-mini S&P 500 futures volatility over the next 50,000 contracts, whatever the number of hours or milliseconds it takes to exchange that volume. High-frequency traders do not rely on forecasts of volatility over a chronological time horizon because they must control the volume of their inventory.51


High-frequency traders exploit the inefficiencies in how markets oper-ate. Such inefficiencies arise, for example, from tick size specifications, matching engine protocols, or latency issues in sending orders both within and across markets. In order-driven markets, bids and offers never arrive simultaneously, so there is always a need for a liquidity provider, that is, the economic agent that bridges the mismatch in time between buy orders and sell orders. Historically, human market makers have filled this role of providing continuous quotes to help facilitate trading. With technology and regulatory innovation, markets and trading are faster, so the human market maker’s ability to quote effectively has become harder to accomplish. Machines and algorithmic processes have largely sup-planted the human-driven liquidity providers, and some high-frequency technologies can now fill the role of the specialists and market makers of old. The result can be a substantial increase in the efficiency of US equity markets, in terms of spreads coming in and transaction costs falling.

A distinguishing feature of high-frequency strategies is that they use a new type of information. Traditionally, informed traders in markets were those who had better information on asset fundamentals—at lon-ger time horizons, fundamental information determines asset prices. In the very short horizon, information related to the trading process pre-dominates. To put the speed of typical high-frequency traders into per-spective, NASDAQ reported an average of more than 580,000 orders per second, which translates into approximately a two-millionths of a sec-ond for a round-trip order—speeds for high-frequency are even greater.52 High-frequency trading generally exploits information related to order flows and the structure of the book to predict where market prices are going both in a single market and across markets. High-frequency traders implement strategies using interconnected electronic networks designed to take advantage of predictable behaviors in order flow.

The strategies of high-frequency traders are diverse and are tailored to the market structure of the particular asset class. For example, a high-frequency algorithm to trade equities would implement a very differ-ent strategy from a high-frequency algorithm to trade futures on equity indexes. While cash equity markets are fragmented and decimalized, the markets for equity futures are not. Similarly, fixed-income trading


algorithms must have special defensive features built in to protect the trader from the shocks arising from public information events such as Treasury auctions results or scheduled government data releases. Fixed-income futures are cointegrated, meaning that individual contracts are not independent of each other due to linkages with the term structure, varying maturities, and the like. Algorithmic strategies must take account of inherent tendency for prices to move congruently.

High-frequency trading is based on a thorough understanding of the price formation and trading volumes, and the success of these strategies illustrates that prices are not random walks over very short time intervals of smaller than a millisecond. The question is whether the high-frequency trade translates into a new investment paradigm. In a 2000 speech then-Chairman of the US Federal Reserve, Alan Greenspan noted that electronic finance represents an acceleration of the process that noted economist Joseph Schumpeter many years ago termed “creative destruction”—the continuous shift in which emerging technologies push out the old.53 The advances in technology pose some challenges for financial institutions and markets and for policymakers. Some institutions inevitably will suf-fer erosion of their franchise values as competitors, new and old, prove more adept at tapping the potential gains from the new technology. More research is needed to fully understand the effects of HFT on liquidity and price discovery more generally, and on other participants, such as institu-tional investors in particular.

Capital mobility and market liquidity

Trading requires capital irrespective of the market structure in which trad-ing occurs. Even seemingly “free” trades such as short-selling or collateral-ized borrowing require capital in the form of margin. For example, traders can use a repurchase agreement (repo), a form of collateralized borrowing as a source of short-term cash. In a typical repo the trader uses securities such as corporate or Treasury bonds as collateral for their borrowing. But the trader cannot borrow the entire price and needs to finance the margin or haircut, the difference between the security’s price and the collateral value, with their own capital. The total margin cannot exceed a participant’s capital.


Trading by any market participant is therefore contingent on their capital, which comes in the form of either having sufficient cash or having the ability to obtain credit at acceptable terms.

A market participant’s access to capital depends on, among other things, the institutional features of the market in which they operate, their credit rating, and access to debt capital, but also on the regulatory capital requirements tailored to their particular industry. Hedge funds face little regulation, and their capital consists of their equity capital that is supplied by the investors. Hedge funds may have some amount of per-manent equity capital, but investors can withdraw their equity capital after a initial lock-up period. Commercial and investment banks’ capital consists of equity capital in addition to its long-term borrowing, includ-ing secured credit lines.54 Banks also raise money using short-term uncol-lateralized loans such as commercial paper and promissory notes, or in the case of commercial banks, demand deposits. The trading activity of banks is largely based on collateralized borrowing such as repo. The mar-gin again needs to be funded using the bank’s own capital—the details of these arrangements for dealer banks are more complicated than those for the hedge fund.55

Commercial banks are regulated and subject to the Basel Accord, supervised by the Federal Reserve System of the US banks. Broker-speculators in the United States, including banks acting as such, are sub-ject to the SEC’s “net capital rule” (SEC Rule 15c3-1). This rule stipulates that, among other things, a broker must have a minimum “net capital,” which is defined as equity capital plus approved subordinated liabilities minus “securities haircuts” and operational charges. Market makers are in principle subject to the SEC’s net capital rule, but the rule has special exceptions for certain market makers, such as NYSE specialists. These specialists’ main regulatory requirements are those imposed by the exchange on which they operate. Since a specialist has an obligation to make a market, complying with the funding constraint is especially cru-cial for them since it directly impacts their ability to supply liquidity for exchange trading.

The capital requirements of market makers, broadly defined to include all market participants who provide liquidity, such as brokers,


dealers, specialists on the NYSE, or dealers on NASDAQ, highlights several important aspects of the market-making activity.

In his presidential address to the American Finance Association, esteemed Stanford professor Darrell Duffie argued that “slow-moving capital” inhibits the immediacy with which market makers react to supply and demand shocks.56 Duffie defined “slow-moving capital” as short-term impediments to capital and explained that capital mobility affects mar-ket liquidity and the observed prices of traded securities. Capital can be restricted temporarily, for example, if margins on repurchase agreements (collateralized borrowing) increase, or more permanently, for example, in response to changes in regulatory capital requirements. Consider, for example, what happens when arbitrageurs who specialize in certain assets suffer significant losses in capital.

A common hedge fund strategy before the 2007/2008 financial crisis was the credit default swap-corporate bond basis trade. A credit default swap (CDS) is similar to an insurance contract in that it provides the buyer of the CDS with protection against the default of a corporate bond issuer. The buyer of default protection receives a single payment from the seller in the event of a default of the reference entity, the corporation that issued the bond, to cover their default losses.57 In exchange, the seller receives a series of payments from the buyer for the duration of the CDS or until the default of the issuer, whichever comes first. The payments from the buyer to the seller, the CDS rate, is an annual premium paid for the coverage of default losses should the issuer default before maturity of the CDS contract. Because the CDS reflects the credit risk of a corporate issuer, it trades in tandem with the issuer’s bonds with similar ranking and maturity.

The CDS-corporate bond basis is the difference in the premium pay-ment on the CDS and the yield spread on the corporate bond. To the extent that the basis becomes materially negative, an arbitrageur buys CDS protection and contemporaneously buys the corporate bond.58 The arbitrageur pays the premium on the CDS and receives the yield on the corporate bond, which is greater than the payment on the CDS. In a fric-tionless market with no impediments to trade, the CDS-bond basis will be mean reverting due to the actions of these arbitrage traders. During


normal market conditions, the CDS-bond basis is essentially zero (insti-tutional details such as counterparty risk can, however, cause the basis to be slightly different than zero).

The arbitrageur executed a profitable converge trade. In reality, the arbitrageur requires capital for this trade, for example, using a repur-chase agreement that requires an initial margin payment—this effectively reduces the profit to the arbitrageur. In the event that the financing or the margin payment becomes prohibitively expensive, arbitrageurs will not execute such basis trades, causing the CDS-bond basis to diverge from zero for an extended period of time. This is exactly what transpired dur-ing the 2008 crisis period. The CDS-corporate bond basis was extremely negative across broad portfolios of investment-grade and high-yield bonds, as shown in Figure 3.2. For the typical arbitrageur, borrowing was just too expensive. According to J.P. Morgan, the initial margin on cor-porate bonds increased from 5 percent in June 2005 to 10 percent in June 2008. In October 2008, the margin increased to 20 percent to 25 percent, and financing for many hedge funds was simply not available.59

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High Grade All CDS-Bond Basis High Yield All CDS-Bond Basis

Figure 3.2 The corporate bond credit default swap basis for high-yield and investment grade bonds.


The most plausible explanation was the shortage of capital held during the financial crisis by dealer banks, which was exacerbated by the increase in haircuts on corporate bonds in the repurchase market. Dealers hold buffer inventories of securities they intermediate. In the event that their balance-sheet capacity is depleted, their ability to intermediate markets is reduced.

A dealer that intermediates a market in CDS basis trades locks up a substantial amount of their balance sheet capacity, both to make markets in the underlying bond, which calls for finding and holding the underly-ing bonds, and to handle two credit default swap counterparties, one with the arbitrageur and one with the counterparty taking the opposite side of the trade.60 As dealers regained balance sheet capacity with improvements in market conditions and some capital raising, the CDS basis reverted to more normal levels in 2009, as illustrated in Figure 3.2.

Other impediments to a market participant’s capital, such as regulatory changes, are more permanent. For example, consider the case of bond trad-ing, which takes place in the OTC market dominated by a limited number of dealers. These dealers are also the main shock absorbers when supply and demand imbalances arise in the bond market. Regulatory initiatives following the 2007–2008 financial crisis increased the level of capital that market makers were required to hold against bonds. Regulators’ objectives with these regulation were to improve market makers’ resilience and stabil-ity, but instead it increased the capital cost of holding bonds, which made it too expensive for them to hold bonds on their balance sheets.61 As a result, market makers were less willing to trade bonds, and a number of dealers withdraw fully or partially from market-making and proprietary trading activities in fixed income. Dealer inventories in corporate bond markets have fallen by nearly 79 percent to around $59 million from a high of $286 billion in late 2007. During the same period, the issuance of US investment-grade corporate bond markets increased by about 50 percent to more than $3 trillion as issuers took advantage of low rates and investors search for yields. The mismatch between low trading inventories and high demand and issuance is one factor that is restraining liquidity in corporate bonds.62

Market liquidity in fixed-income bonds has declined amid a ballooning size of new debt issuance. For example, US Treasury issuance increased


from $4.6 trillion in 2006 to $11.9 trillion in 2013, and corporate issu-ance increased from $5.5 trillion to $9.8 trillion over the same period. But liquidity has declined, as can be seen in the decline in the trading ratio across fixed-income securities, shown in Table 3.1. The trading capacity of the market has not kept up with the substantial growth in the size of the market.

The importance of the liability structure of dealer balance sheets is not new. We explore the link between a dealer’s balance sheet composition and market liquidity in chapter 5, and between balance sheet composi-tion and regulatory policies in chapter 6.

The importance of capital applies more broadly to all market partici-pants, including investors and speculators. When capital is restricted, market participants become reluctant to take on positions, especially capital-intensive positions in high-margin securities that force them to scale down from some markets, leading to wider bid-ask spreads, or to withdraw completely, leading to a trading halt. In their seminal paper, Professors Markus Brunnermeier and Pedersen developed a framework for understanding the causal link between funding and market liquidity.63 Traders provide market liquidity, and their ability to do so depends on availability of funding. Conversely, traders’ funding, that is, their capital and margin requirements, depends on the assets’ market liquidity. In a typical collateralized borrowing transaction, such as repurchase funding (repo), the borrower must post margin to cover the risk that they may

Table 3.1 US fixed-income market size and trading ratio

Fixed Income Market Amount Outstanding ($ Trillion) Trading Ratio

2006 2013 2006 2013

US Treasuries 4.3 11.9 31.6 12.4

Corporates 5.5 9.8 0.8 0.5

Mortgages 8.4 8.7 8.9 6.5

Municipals 3.2 3.7 1.9 0.8

Agencies 2.6 2 7.1 0.8

Note: The trading ratio is the annual dollar volume divided by the total debt outstanding.

Source: SIFMA, April 2014 and BlackRock Investment Institute.


not be able to pay for the securities they are buying or deliver the securi-ties they are selling. However, margin requirements depend in part on the security’s market liquidity. Under certain conditions, margins are destabilizing, and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. We revisit the Brunnermeier and Pedersen framework in more detail in chapter 6 in the context of asset pricing.

The relationship between a market maker’s access to capital and their ability to provide market liquidity also furthers our understanding of market liquidity, and in particular it motivates the commonality in mar-ket liquidity. Bid-ask spreads widen simultaneously on many security markets when funding to market makers is restricted, affecting liquid-ity in all securities. This insight may help explain why liquidity can be correlated across stocks and across bonds and stocks. Comovements in liquidity for stocks handled by the same specialist on the NYSE are related to this ability to provide liquidity. This suggests that an increase in the cost of capital or increased risk exposure for a specialist leads to a simultaneous drop in liquidity for the stocks assigned to that specialist.64 Another empirical study showed that the liquidity of corporate bonds drops if the lead underwriter faces funding problems.65 Consider the liquidity of bonds underwritten by Bear Stearns, before being taken over by J.P. Morgan in 2008. In November 2007, Bear Stearns wrote down $1.62 billion and booked a fourth-quarter loss, and in December 2007, there was a further write-down of $1.90 billion. During these months, the liquidity of bonds underwritten by Bear Stearns decreased, compared to bonds underwritten by others. On March 16, 2008, Bear Stearns was taken over by J.P. Morgan. The liquidity gap between bonds underwrit-ten by Bear Stearns and by others returned to zero in June 2008 after Bear Stearns shareholders approved J.P. Morgan’s buyout of the invest-ment bank on May 29, 2008.

Concluding thoughts

Technology and new regulatory policies are revolutionizing the way mar-kets operate forcing us to adjust to new realities of increased transaction


speeds, new paradigms of trade execution, new mechanisms for dissemi-nating trading information, changes in regulatory capital requirements, and moves toward the central clearing of some OTC products. However, the fundamental purpose of markets has not changed—financial mar-kets exist to provide liquidity and enable price formation. This chapter highlights some of the core structural drivers of market liquidity—these will not change, but they may be recast as we adjust to the new market realities.

Introduction

Traditional asset pricing models are based on the notion that aggregate market risks rather than individual risks are priced in a market that has reached an equilibrium between supply and demand from participants. The assets prices under this paradigm generally agree with the funda-mental value of the asset, and all you need for asset pricing is knowledge of the cash flows or payoff and a specification of the discount factor. This traditional economic paradigm, discussed in chapter 1, further assumes that markets are frictionless, or perfectly liquid, and that capi-tal is freely available. Yet this traditional paradigm has limited ability to explain empirically observed market behaviour because it either dis-misses the issue of market liquidity as a friction or accounts for market liquidity by adding a transaction cost to the fundamental value. Market liquidity is typically incorporated as an exogenous transaction cost—an afterthought to asset pricing. The simplicity of this view is appealing, and for the ignorant market participant it may be sufficient. It acknowl-edges the difference between the transaction price and the fundamental value of an asset, and further attributes this difference to the costs of trading. Incidentally, adding trading costs violates the basic assumption of frictionless markets on which most classical asset pricing models are based.

Simply adding transaction cost to the fundamental value does not fully capture the intricacies of market liquidity or adequately explain differ-ences between fundamental values and traded prices. To shed light on the

4 Asset Pricing and Market Liquidity


limitations of this simplistic approach, we consider two examples of asset price anomalies that can be explained by market liquidity.

In our first example, we look at pricing anomalies in government bonds in which pricing is not contaminated by other factors such as default risk, which is prevalent in corporate bonds. This allows us to focus on the pricing differences due to factors such as market liquidity. In a 2004 study, Professor Francis Longstaff observed that the aver-age yield of Resolution Trust Corporation bonds was between ten and sixteen basis points higher than the average yield of comparable Treasury bonds and that these differences varied significantly over time.1 Longstaff selected the bonds in his study to be comparable along other dimensions—in particular, the bonds had corresponding maturi-ties and zero coupon payments. The tax treatment for Resolution Trust Corporation and Treasury bonds are similar and there is essentially no difference in the default risk of Treasury bonds and Resolution Trust Corporation bonds—the US government guarantees full payment of Resolution Trust Corporation bond coupons and fully collateralizes their principal with Treasury bonds. However, unlike Treasury bonds, Resolution Trust Corporation bonds are rather obscure securities, in limited supply and therefore less liquid than their widely traded Treasury bond cousins. This comparison illustrates that investors are willing to accept a lower return on the more liquid treasury bonds when compared to the less liquid Resolution Trust Corporation bonds.

In our next example, we consider the Standard & Poor’s (S&P) 500 equity index, one of the most commonly followed equity indices and representative of the US stock market. It is well know that adding a stock to the S&P 500 equity index results in an “index pop” or “index effect,” while deleting a stock from the index cause it to suffer a temporary price decline.2 These types of price anomalies can be explained as disguised effects of market liquidity: the S&P 500 firms are followed more widely than individual securities and there is generally more information avail-able on such firms, that reduces information asymmetry between inves-tors and consequently improves the market liquidity of these securities. A better understanding of asset pricing implications of market liquidity may expand the utility of stock indexes as financial tools.

Asset Pricing and Market Liquidity 77

Neither ignorance nor simply relegating market liquidity to trans-action cost could have fully explained either one of the price phenom-ena illustrated by these examples. What we need are new asset pricing models.

In this chapter, we discuss several frameworks of asset pricing that show us how to incorporate a market liquidity premium into asset prices and returns. Each of these frameworks develops a model by considering certain aspects of market liquidity. In each framework, we focus on spe-cific features of the liquidity process that will also further our knowledge of market liquidity more generally.

The first framework illustrates the relationship between the bid-ask spread and asset prices and shows how this relationship is affected by investors’ holding period. The second framework assumes that liquidity can also be generated as an endogenous part of the trading process such as trading in the over-the-counter (OTC) market, in which an inves-tor who wishes to transact, must search for a counterparty, all the while incurring opportunity or other costs until one is found. This search-and-bargaining model, developed by esteemed Stanford Professor Darrel Duffie, differs from the simple bid-ask spread in that liquidity is endog-enous to the trading process in which prices and liquidity are jointly determined.

The next framework relies on a combination of behavioral econom-ics and finance theory to arrive at an asset pricing model with liquidity motivated by the failure of the no-arbitrage principle discussed in chap-ter 1. Classical finance theory is agnostic to behavioral aspects of trading, and therefore cannot be used to explain the failure of arbitrage in certain instances.

These frameworks quantify the effects of the level of market liquidity on asset prices.3 Transaction cost reduces the return to investors, but there is also a commonality in liquidity, which implies that liquidity risk is necessary to characterize a financial asset fully. The liquidity-adjusted capital asset pricing model discussed in the section titled “A mean- variance framework for pricing liquidity risk” incorporates liquidity risk and advances our understanding of liquidity as a random time-varying endogenous cost.


Exogenous (“add-on”) cost of trading

The bid-ask spread

This section illustrates the relationship between the level of liquidity and asset prices based on the insights and simple model developed by the pioneers of market liquidity research, Professors Yakov Amihud and Haim Mendelson.4 Trading costs are the direct and indirect costs asso-ciated with trading a security. Direct costs include brokerage fees, trans-action taxes, and other trade-processing fees that are easy to measure. The trading cost we look at in this section is the bid-ask spread. The bid and ask prices quoted in a market set the prices at which customers can buy from or sell to market makers. In this section, we consider the bid-ask spread more broadly as a measure of market liquidity.

The Amihud-Mendelson model shows an inverse relationship between transaction cost and return, whereby an increase in transaction cost causes a pari passu decrease in the return. An interesting outflow of this model is that the relationship between the market return and the transaction cost is not linear, but the gross return increases with bid-ask spreads at a decreasing rate or, put differently, return is a concave function of the bid-ask spread.5 The relationship between stocks’ excess monthly return and the bid-ask spread for a given level of systematic market risk is shown in Figure 4.1.

The concave relationship reflects the theoretical insight offered by the Amihud-Mendelson model, which is that in equilibrium, less liquid securi-ties are allocated to investors with longer holding periods. These investors’ longer holding periods mitigate the compensation that they would have required for higher transaction costs on less liquid securities. Empirical research by Professor George Constantinides showed that large transaction costs typically have small price effects, confirming the concave relationship between bid-ask spread and return found by Amihud and Mendelson.6

A model of bid-ask spread and return

Consider the following simple model of bid-ask spread and return. An investor buys a security that he plans to sell after h periods. During


this time, the security does not pay dividends or interest. The market is illiquid, and the proportional bid-ask spread at date t is denoted by st (our intention here is for the bid-ask spread to refer more broadly to the transaction cost). The bid and ask prices at that time are given by

a ms

t tt= +

1

2 (4.1)

and

b ms

t tt= −

1

2. (4.2)

We assume that the midquote is equal to the fundamental value of the security, mt = μt. Suppose that investors require a return of r per period on the security, given its risk characteristics. For example, for a riskless security like a Treasury bill, the required return is simply the risk-free rate. If the market were perfectly liquid (i.e., st = 0 at any time), then the expected return on the Treasury bill would be the risk-free rate.

Assuming that the market is not perfectly liquid, the maximum price that the investor is willing to pay at date t for a security with a cash

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xces

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thly

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urn

Bid-Ask Spread (%)

Figure 4.1 The relationship between a stock’s excess return and the bid-ask spread.

Source: Y. Amihud, H. Mendelson and L.H. Pedersen, 2013, Market Liquidity: Asset Pricing, Risk and Crises, Cambridge University Press. Figure PI.1, page 5.


flow at a future date (t+h), given by the standard discounted cash flow model, is

ab

rtt h

h=+( )

+

1. (4.3)

We substitute the formulations of the ask price and the bid price in equa-tions (4.1) and (4.2), into (4.3) to arrive at the relationship expressed in equation (4.4):

µ µtt

t ht h

h

s sr

12

12

11

+

= −

+( )+

+ . (4.4)

Next, we rewrite equation (4.4) to express the current value of the asset at its discounted future value, adjusted for current and future transac-tion costs:

µ µt t h h

t h

tr

s

s=

+( )

−

+

+

+

11

12

12

. (4.5)

The last term is a measure of the illiquidity since it decreases respectively in both the current and future transaction costs st and st+h. The funda-mental value at date t is correlated with illiquidity: the greater the current estimate of the future spread, the higher the transaction costs for inves-tors and the lower the value of the asset to them. This is consistent with our example, in which the more liquid Treasury bills traded at a premium over notes, notwithstanding their identical payoffs at maturity.

In a liquid market, the bid-ask spread would also not depend on the quantities being traded. Empirical results show that the spread quoted in markets is not generally an exact reflection of transaction costs because certain transactions may be traded either inside or outside the quoted spread. An alternative measure is the effective spread defined as the dif-ference between the execution price and the midpoint of the quoted bid-ask spread.

The effective spread is defined as

s d p mte

t t= −( ), (4.6)


where d is an indicator variable assuming the value of 1 if the transaction is buyer initiated, or –1 if the transaction is seller initiated.

Notwithstanding its simplicity, a limitation of bid-ask spread mea-sures is that it reflects liquidity at a particular time. In asset pricing and risk management applications, it is important to account for the fact that liquidity is uncertain and varies over time.

The Roll measure of effective bid-ask spread

The economist Richard Roll proposed a measure for inferring an effective bid-ask spread directly from transaction prices.7 The Roll measure takes the size of the bid-ask spreads as a given. The spread calculated using the Roll measure is akin to an effective spread (the effective spread is expressed in terms of the transaction prices as defined in equation [4.6]). Trades can be executed within the quoted bid-ask spread, which can make the effective spread a more accurate reflection of execution costs. The Roll measure may be best suited to exchange trading with an order book. The motivation for including it here is that it lays the groundwork for the idea that there is a relationship between the covariance in the security’s return and market liquidity, which forms the basis of some frameworks we dis-cuss in chapter 5.

The Roll measure is based on the following insight from trading in a limit order book: in the event that an order hits the bid-ask prices at random, the transaction prices bounce back and forth between them, straddling the midquote. The transitory deviations around the midprice are called the bid-ask bounce. Intuitively, this bounce engenders negative serial correlation in transaction-to-transaction returns even when the underlying value (midquote) follows a random walk. To see this, suppose that a buy market order arrives at time t, followed by a sell market order at time t+1. The first trade is at the ask price, the second at the bid price. Thus, the return between dates t and t+1 is negative. One accordingly expects a positive return between t+1 and t+2, the time of the subsequent transaction. Either the next market order is again a sell order executed at the bid price (in which case the return from t+1 and t+2 is zero) or it is a


buy market order executed at the ask price (in which case the return from t + 1 to t + 2 is positive). A similar argument shows that, after a positive return, one would expect a negative return. Roll exploits this intuition to construct an estimator of the bid-ask spread based entirely on the serial covariance of returns.

Suppose that a security’s fundamental value, captured by the midquote, follows a random walk:

V Vt t t= +−1 ε . (4.7)

where εt is a zero mean white noise such that E(εt) = 0 for all t and E(εt εs) = 0 for all t ≠ s. This variable represents the change in the value of the security due to new information between time t − 1 and t. As E(εt) = 0, the expected fundamental return is zero. This is a reasonable assumption if the time interval is assumed to be small, for example, one day.

Assume that the bid-ask spread S is constant over time. The observed transaction price can be expressed as

P V St t t= +

2δ , (4.8)

where δt is a random indicator of whether the transaction at time t took place at the bid or the ask price, δt = 1 if the transaction is initiated by a buyer, or δt = −1 if the transaction is initiated by a seller. In particular, the ask price at which a market buy order is executed by the dealer and the bid price at which a market sell order is executed by the dealer are

Askt tV S= +2

and

Bidt tV S= −2

.

The transaction-to-transaction price change is

∆ ∆P P P V St t t t t t= −( ) = + +−1 2

δ ε . (4.9)


Roll made the following assumptions on the order arrival process:

(a) Order flow is balanced: market orders are equally likely to be a buy or a sell order. In other words,

P Pt t( ) ( ) ,δ δ= = = − =1 1 1

2 or equiva-

lently, E(δt) = 0 for all t.

(b) There are no autocorrelations in orders. Buy and sell market orders are serially uncorrelated, in other words, E(δt δs) = 0 for all t ≠ s.

(c) There is no effect on the midquote. Market orders are assumed to carry no news, meaning that they are uncorrelated with current and future innovations in fundamentals: E Et t t t( ) ( )δ ε δ ε= =+1 0 for all t.

(d) There is constant (zero) expected return. The fundamental value fol-lows a random walk so that E P P Et t t−( ) = ( ) =−1 0ε for all t.

Under this set of assumptions, Roll’s measure follows:

E P Pt t−( ) =−1 0

cov ∆ ∆P P St t, .−( ) = −1

2

4 (4.10)

Roll’s measure captures a useful measure of transaction cost and it uti-lizes readily available transaction prices as opposed to requiring data on bid-ask spreads. The Roll measure is useful for longer-horizon empiri-cal studies since it rests on the assumption that the fundamental value or midprice following a random walk and market bid and ask transac-tions are balanced. Professor Hans Stoll, in his 1999 Presidential address to the American Finance Association, used the Roll measure to compare the magnitude of trading costs between stocks traded on the New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotations (NASDAQ). Stoll studied 1,706 NYSE stocks and 2,184 NASDAQ stocks in the three months ending on February 28, 1998. He estimated Roll measures of 3.81 cents on the NYSE and 11.15 cents on NASDAQ after controlling for difference in market capitalization between these exchanges.8 The Roll measure was also within the quoted spreads of 7.9 cents on the NYSE and 12.6 cents on NASDAQ. Stoll’s study of liquid-ity differences between stocks on the NYSE and NASDAQ further high-lights the importance of market structure, as we discussed in chapter 3.


Market impact measures of Amihud and Kyle

The market impact measures build on the idea that a given volume of securities in a liquid market can be traded without significantly affecting their prices.

Market impact measures were spearheaded by respected professor and fellow of the American Society of Finance Albert Kyle based on insights from market micro-structure.9 Kyle proposed that market makers cannot distinguish between order flow generated by informed traders and order flow that may indicate uninformed or noise trading. Market makers set prices that are increasing functions of the imbalance in the order flow, which creates a positive relationship between transaction volume and price change. The price impact of a particular trade will therefore be smaller in a liquid market. Kyle showed that market “depth,” a characteristic of a liquid market, discussed in chapter 1, is indeed a result of the optimizing behav-ior of market makers. Kyle’s liquidity measure is the order flow necessary to induce prices to rise or fall by one dollar. The measure is implemented using intradaily transaction data that may not always be available.

A related market impact measure that can be implemented using read-ily available daily transaction prices is the popular Amihud measure, proposed by Professor Yakov Amihud. The Amihud measure uses the intuition that a security is less liquid if a given trading volume results in a greater move in its price.10

The Amihud measure utilizes data on traded security prices and the average trading volume over a given time period defined as

Amihud measure = Average of the per period Absolute returnTTraded volume

. (4.11)

The traded volume for stocks is the product of the number of shares traded on a given day and the closing price at the end of the day. The Amihud measure can be interpreted as the price response associated with one dollar of trading volume—serving as a rough measure of the price impact. The popularity of this measure lies in the ease with which it can be implemented using accessible transaction prices and the fact that it can be implemented with time series data to capture changes in market liquidity over time.


Liquidity premium of a search-and-bargaining model

As discussed in more detail in chapter 3, one of several distinguishing fea-tures of the OTC market compared to [a] typical exchange-traded mar-ket, is the presence of a market maker or dealer who intermediates trade. Price formation in the OTC market occurs through a bargaining process between market makers and their customers. Trading in the OTC market is typically slower than on exchanges, because searching for counterpar-ties who can ensure optimal execution takes time. An everyday exam-ple of this type of search-and-bargaining market is the residential real estate market, in which home prices are influenced by the process of an imperfect search, such as impatience on the part of sellers and the out-side options of buyers and sellers, and imperfect search factors, such as weather, the school calendar, and other personal changes.

In the standard treatment of dealer markets, dealers hold an inventory of securities, and manage liquidity, that is, transaction cost, to control their inventory.11 Another standard treatment assumes that market mak-ers are exposed to adverse selection by “informed” traders, such as firm insiders. Transaction cost represents the market maker’s compensation against adverse selection due to information asymmetry.12 Both these views, inventory management and scenarios of asymmetric information, define market liquidity as compensation for a risk faced by the market maker. The compensation of the market makers comes in the form of the bid-ask spread, an exogenous addition to transaction prices.

Professors Darrell Duffie, Nicolae Gârleanu, and Lasse Pedersen expanded our thinking about market liquidity by shifting the focus from the market maker to the behavior of a typical customer or buy-side inves-tors in the OTC market.13 Shifting the focus to the customer reflects the reality of OTC markets, in which bid and ask prices are partly determined by investors’ outside options. For example, consider a multibillion-dol-lar investment manager and a portfolio manager at regional bank in the US Midwest who both want to execute an interest rate swap to hedge their portfolio. The larger portfolio manager has more counterparties to choose from than the typical regional bank manager, and he can therefore “shop around” for the optimal trade price, to ensure lower execution costs relative to smaller investors. The typical inventory model does not take


into account this type of differential treatment across different investors, which clearly does not reflect the reality of this market. Customers are heterogeneous agents, and many factors such as the frequency and the size of their trading, and their credit ratings affect their accessibility to other market makers and their ability to secure counterparties.

In their setup, Duffie, Gârleanu and Pedersen negate the need for dealers to hold inventory by assuming that market makers access the interdealer market to augment their trading. They show that in a mar-ket with heterogeneous investors, sophisticated investors with bet-ter access to market makers receive tighter bid-ask spreads because of improved outside options as compared to other investors. According to this view, the bid-ask spread has a cross-sectional component that could be different for the same securities transacted by different inves-tors. Inventory-based models do not imply such a differential treatment across investors. This theory adds another tool to the kit and highlights a different aspect of market liquidity that is consistent with behavior in certain OTC markets, such as interest rate swaps and foreign exchange, in which asymmetric information is limited. Investors rarely have material private information about the current level of interest rates, so standard information based explanations of bid-ask spreads are not compelling in these markets.

The Duffie, Gârleanu, and Pedersen framework builds on the dynamic interaction between dealers and their customers in a dynamic search-and-bargaining setup. The search-and-bargaining model expands our tool-set of liquidity models in two ways: it adds search costs as an additional consideration of market liquidity, and it relates the liquidity premium to the dealer’s bargaining power and search costs, essentially assimilating liquidity as an endogenous component of the trading process.

Setup of the search-and-bargaining model

In this section, we analytically derive an asset pricing model in a search-and-bargaining market. The search-and-bargaining model developed below is an asset-pricing variant roughly based on the famous Diamond coconut model. The coconut model was devised by Peter Diamond, the 2010 Nobel laureate to understand behavior in labor markets.14 The Diamond model


also captures the essence of a search-based economy by envisioning an island populated by individuals who can only consume coconuts. Agents in this model are always in one of two states: they are both carrying a coco-nut and looking for someone with whom to trade it, or they are searching for a palm tree in order to possibly pick a coconut.

We can contract a hypothetical market where trading between agents mirrors Diamond’s island economy. This market has two kinds of agents: investors and market makers. We assume all agents to be risk neutral and infinitely lived. Agents engage in the repeated trade of a single nonstor-able consumption good.15 Market makers hold no inventory and maxi-mize profits. Market makers have access to an interdealer market on which they unload their positions. Short sales are not allowed.

Investors can invest in two types of securities: a riskless asset paying one dollar per period forever, referred to as a consol bond,16 and a risk-free bank account paying an interest rate of r. The consol bond can be traded only when an investor finds a market maker according to a ran-dom search model described in more detail below. The bank account rep-resents a liquid security that can be traded instantly.

In any given period, investors can choose not to hold the asset or can hold at most one unit of the asset. We assume a fraction q of investors is initially endowed with the asset. Since investors can hold at most one unit of the security, this would be equivalent to saying that there is a finite sup-ply, q, of the consol bond. The investors are not all the same: some inves-tors, referred to as “high rollers,” bear no cost for holding the asset. Other investors, referred to as “low rollers,” incur a holding cost of c per period, so their cash flows from holding the asset are reduced by the holding cost, $(1-c) per period. The holding cost of low rollers may reflect constraints, such as a less favorable tax rate than that faced by high-roller investors, higher financing cost, and a lower personal use of the asset or the imme-diate need for cash. Assume that the low roller’s intrinsic liquidity state is “low.” At the end of each period, investors switch from being low rollers to being high rollers and vice versa, with a probability ψ.

To summarize, there are four types of investors, classified according to their ownership of the asset and by their per-period holding cost: (i) πh

o: high rollers who own the security; (ii) πh

no: high rollers who do not own the security; (iii) π l

o: low rollers who own the security; and (iv) π lno: low


rollers who do not own the security. The full set of investor types shown in Table 4.1 is I = { , , , }π π πh

ohno

lo

lnoπ , where the subscript “l” and “h” indi-

cates the investor’s liquidity state, low or high. The superscript “o” and “no” distinguishes respectively between owners and nonowners.

The profit of high rollers is maximized when they own the asset; there-fore, high-roller nonowners want to buy the asset. The profit of low roll-ers is maximized when they do not own the asset; therefore, low-roller owners want to sell. When two agents meet, they will trade, only if doing so is mutually beneficial. Buyers and sellers need to search for a market maker in order to execute their orders. When an investor meets a market maker, they bargain over the terms of the trade. Investors sell to market makers at the bid price, b, and investors buy from market makers at the ask price a.

We assume that the probability that the investor finds a market maker in any given period is φ, so that on average the investor will have to search for 1/φ periods before execution of his order. This delay generates a search cost for low rollers, who bear the holding cost, c, in each period so that their expected search cost is c / .φ The equilibrium price depends on agents’ outside options, which will be reflected, among the other vari-ables, in the bid and ask prices, b and a.

The price paid by investors for the security is as follows:

ar r z

z S= −+( )

− −

1 21

1 12

ψ φ , (4.12)

where S is the market maker’s bid-ask spread and z is an index of the mar-ket maker’s market power. We can also think of this index as representing an investor’s access to multiple, competitive market makers. The ask price in equation (4.12) is the present value of the payments from the asset, 1/r reduced by a liquidity premium.17

Table 4.1 Investor types in the search-and-bargaining model

Owner Nonowner

High roller πho πh

no

Low roller π lo π l

no


Consider a special case of the model, where we assume investors do not expect their valuation to change over time, that is, ψ = 0, and the liquidity premium vanishes. The ask price reduces to the fundamental value of 1/r. High-roller nonowners expect to earn $1 forever once they buy, so they are willing to pay at most the discounted value of payment the $1 (their holding period being infinite).

Next, consider trading in a market with search-and-bargaining fric-tions, modeled as ψ > 0. The second term in equation (4.12) does not van-ish, and the trade price of the asset is less than the fundamental value due to the liquidity premium. The liquidity premium is proportional to the bid-ask spread S charged by market makers and increases in the bargain-ing power of the market maker. A value of z = 1 represents a monopolis-tic market maker, and smaller values are accordingly associated with less maker power and a lower cost of trading to investors, assuming every-thing else stays the same. The liquidity premium decreases if it is easier to meet a market maker. The investor sustains his holding cost for a shorter time, and his expected search cost c /φ is small.

We mathematically summarize the narrative explanation of trading in the hypothetical OTC market discussed in the preceding paragraphs in equation (4.13). The bid-ask spread in the latter market is expressed as

S a b z cr z

= − = +( )+( )+ −( ) −( )

12 2 1 2 1ψ ψ φ

. (4.13)

The expression for the bid-ask spread shown in equation (4.13) is derived in the Appendix. This model predicts that the spread is increasing in the holding costs c and in dealers’ bargaining power z: when their holding costs are high, sellers’ valuation of the asset is smaller, and if dealers have substantial bargaining power, sellers will be forced to accept a low price. Hence, on both accounts the bid price quoted by dealers will be low. The effect on the spread of the probability φ of meeting a dealer is ambiguous.

Numerical example

We illustrate some of the search effects on asset pricing and market making with a numerical example. Figure 4.2 shows the market maker’s


bid and ask prices as a function of the probability of finding a market maker.

Assume that market makers have some bargaining power, z = 0.8, and that interest rate is r = 5%. In the extreme case of a monopolistic market maker, represented by z = 1 in equation (4.13), the bid-ask spread is given by,

S a b cr

= − =+(

.2ψ )

In the case of competitive market makers, shown in Figure 4.2, the bid-ask spread reduces to zero, and the price approaches its fundamental value of 1/r = 20 if the search becomes more efficient. In contrast, with a monopolistic market maker, the bid-ask spread increases with search efficiency, and the price is bound away from its fundamental value. This result is intuitive. If finding market makers becomes more efficient, more trades are executed. If market makers have all the bargaining

18.40

18.60

18.80

19.00

19.20

19.40

19.60

19.80

20.00

1 10 100 1,000 10,000

Pri

ce

Intensity of an Investor Meeting a Market Maker

Price Bid Price Ask Price

Figure 4.2 The estimated mid, bid, and ask prices as a function of finding a market maker.

Source: D. Duffie, N. Gârleanu and L. H. Pederson, November 2005, “Over-the-Counter Markets,” Econometrica, Vol. 73, 1815-1847. Figure 1.


power, the bid-ask spread is higher than if market makers do not have all the bargaining power. The model also shows that investors with bet-ter outside options receive tighter bid-ask spreads.

The theory of limited arbitrage

Empirical results show that the process of arbitrage sometimes fails to bring prices close to the fundamental values implied by standard models. An example of such can be found in the market for US Treasury bonds. The Treasury issues bonds during regularly scheduled auctions, and these newly issued bonds are referred to as on-the-run US Treasury bonds. At any given time, off-the-run Treasury bonds issued at previous auctions can still be outstanding. It is possible to find an on-the run and an off-the run bond with an equal amount of time to maturity and comparable cash flows. However, the on-the-run Treasury bond typically trades at a higher price (or equivalently a lower yield) than a comparable off-the-run Treasury bond. This suggests the following trading strategy. Sell the on-the-run bond short and use the proceeds to purchase an off-the-run issue with a compa-rable maturity date. Hold these trades until the next auction date or until maturity to lock in the yield differential. This strategy involves two securi-ties with similar risk characteristics that trade at different prices, which is a clear violation of the law of one price. If this were truly a theoretical arbi-trage opportunity, why would investors leave this money on the table?

A critical difference between the theory and the execution of the strat-egy in the financial markets is the need for money or capital. In the exam-ple of the Treasury bond strategy, investors would typically buy or sell the bonds in the spot market and execute a repurchase (repo) or a reverse repurchase transaction to finance the trade. However, transacting in the repo market is not “free” because of the need for margins or haircuts, which are usually posted upfront. If you do not have enough wealth for the margin payment, you can employ leverage that will essentially enable you to execute the strategy using borrowed money. Leverage will expose the investor to additional risks, for example timing risk, when the loan needs to be repaid before the mispricing converges, or market risk, when the mispricing diverges. Timing risk requires a premature liquidation of


the position. Market risk requires posting of additional collateral which could lead to the risk of increased borrowing during adverse markets. A classic example is the demise of the hedge fund Long Term Capital Management (LTCM). LTCM implemented trades that relied on no arbi-trage mispricing which they funded using borrowed monies. The ratio of the debt-to-equity in the beginning of 1998 was approximately 25 to 1. In the wake of Southeast Asian financial collapse and trouble in Russia in the spring of 1998, LTCM’s arbitrage strategies started losing money requir-ing them to post additional collateral and us capital to fund margin calls. By December 1998, the portfolio was unwound and the fund was liqui-dated. Several more recent examples of asset mispricing due to failure of arbitrage relationships during crisis periods were discussed in chapter 2.

Arbitrage relationships may also break down due to factors prevail-ing during normal markets. Consider, for example, the removal of a stock from the S&P 500 equity index, discussed in the introduction to chapter 4. Upon the decision by the S&P to remove a particular equity from the S&P 500, the index funds replicating this index, have a high incentive to sell the deleted equity on the effective date. A very small set of investors are actively considering to purchase the deleted equi-ties near the effective date. They require substantial price discounts. It is also likely that many of the investors planned to sell their positions over time to investors who, on the effective date, were not immediately available or aware of the opportunity to buy. Anticipation of the price at the effective date appears to have led to a substantial reduction in price between the announcement date and the effective date.18

Another aspect that distinguishes real-world arbitrage from theoretical arbitrage is the fact that there is a separation between investors who supply capital and investors who implement arbitrage strategies. Pension funds, endowments, and wealthy retail investors with either limited knowledge or limited interest in financial markets typically invest their money with insti-tutional investors such as hedge funds and proprietary trading desks that have the specialized knowledge and skills needed to execute an arbitrage trading strategy. This creates a responsiveness of funds under management to past returns. Using their own money, arbitrageurs typically allocate funds based on expected return. In the event that mispricing increases and


the return decreases, an outside investor may only observe the arbitrageurs losing their money, and without detailed knowledge of the trade, they may withdraw their capital to invest in more favorable strategies or with a dif-ferent investment manager. However, when mispricing increases, which incidentally implies an even higher expected return, the arbitrageurs may have to liquidate their position to return borrowed monies. In such cases, arbitrageurs can become capital constrained exactly when the best oppor-tunities are available. This dynamic could significantly limit arbitrageurs’ effectiveness in achieving market efficiency because they do not have the capital resources to eliminate mispricing. This is particularly problematic during times of crisis, when their actions are most needed, which could lead to mispricing persisting for a longer period of time.

Inspired by these dynamics of real-world arbitrage processes, Professors Andrei Shleifer and Robert Vishny19 developed an asset pricing model. The Shleifer-Vishny model augments the classical asset pricing consid-erations of risk and return with behavioral finance aspects of investment and capital allocation to quantify mispricing due to the failure of arbi-trage. The model belongs to the class of “agency” models—a fusion of classic asset pricing and behavioral finance.

The Shleifer-Vishny agency model

The Shleifer-Vishny agency model considers two types of fictitious agents: third-party investors and arbitrageurs. The arbitrageurs are professional, highly specialized investors, such as large fund managers. Third-party inves-tors are wealthy individuals, endowments, or other investors with limited knowledge of the specific markets. These investors do not trade on their own, but rather invest money with arbitrageurs. Assume that trading is lim-ited to a generic security such as a zero-coupon bond, and that trading can occur in one of three periods. To capture the funding constraints faced by real-world arbitrageurs, we assume that the arbitrageurs can only be active in either the first or the second period, but not in both, and that agents can-not take a position of more than one unit in either security.20

Assume that at date 0 there are two similar zero-coupon bonds, bond A and bond B. Both bonds are correctly priced at date 3. The price of each


therefore equal the fundamental value V. The risk-free interest rate is set to zero, so that in the event of no arbitrage, the price of the two bonds should be equal at each of the three dates.

At date 0, there is an exogenous shock that causes the price of bond A to fall below that of bond B by an amount M0 > 0 such that P0A = P0B−M0. Bond B is priced correctly at P0B = V. Bond A is undervalued, and the size of the mispricing is M0. This price difference presents the following arbitrage opportunity: purchase the cheaper bond B for P0B and sell bond A for P0A collecting a cash flow of M0. There is a risk that the mispricing can increase during the next period, in which case the arbitrageurs would have been better off to wait before implementing the strategy. Assume that the mispricing increases to M1 > M0 with probability κ or disappears with probability (1−κ). At date 2, the assets pay off, so any mispricing is eliminated: P2A = P2B = V.

Before analyzing the prices in this model, consider the theoretical extremes of the model. If we assume arbitrageurs have unlimited resources and trade in a frictionless, efficient market, they will immediately counter-act the mispricing and keep prices in line with their fundamental values. An alternative scenario is one in which arbitrageurs have limited capital, but free access to markets. Even if the mispricing increases, they can replen-ish lost funds by raising more money. In subsequent sections we describe additional versions of the framework, in which investors are forced to pre-maturely liquidate arbitrage strategies and in which arbitrageurs have lim-ited access to additional funding.

Limited arbitrage due to risk of early liquidation

In this version of the model, we assume that the arbitrageur is a fund man-ager and that their allocated capital depends on their past performance.21 Such arrangements are typical in the money management industry, in which hedge fund, pension fund, or mutual fund managers invest either their own money or money from other wealthy individuals.

The arbitrageurs have a choice whether to execute the arbitrage strat-egy at date 0 or at date 1.


Under the first scenario, the arbitrageur chooses not to intervene at date 0, and to intervene at date 1 only if mispricing persists. By definition, he has zero cash flows at date 0. At date 1, he has the opportunity to set up an arbitrage portfolio only of the mispricing of bond A increases; therefore, he gets a cash flow M1 with probability κ. If instead the mispricing disappears, the arbitrageur can no longer intervene profitably, and will get no cash flow. The arbitrageur’s expected profit if he waits to intervene at date 1 is

Πwait M= κ 1.

Suppose the arbitrageur chooses not to wait and decides to intervene at date 0. He then pockets the mispricing M0. With probability (1−κ), the mispricing disappears at date 1, and he chooses to close out his position. With probability κ, the mispricing increases, so that the market value of the portfolio declines from 0 to M0−M1 < 0. In the second scenario, the arbitrageur does not liquidate his position at date 2, so there is no additional cash flow at date 2, and he closes the position at date 3. The arbitrageur manages the investors’ capital, which is sensitive to his per-formance. In this scenario, we are essentially assuming that investors will not remove their capital.

Under the third scenario, the arbitrageur executes the strategy at date 0, when the mispricing persists at date 1. The third-party investors choose to remove their capital from the fund because they cannot tell whether the increase in mispricing is transient or reflects a true loss in value of the asset. The arbitrageur is therefore forced to liquidate his position at a loss, because he sells bond A at P1A = V − M1 and covers his short position in bond B by buying bond B at a price V. We assign a probability ϕ to the likelihood of scenario three. Therefore, intervention at date 0 yields an expected profit equal of

Πdo not wait ( ) .ϕ κφ= M M0 1−

The arbitrageur’s choice hinges on the following trade-off.Under the first scenario, in which the arbitrageur chooses to inter-

vene at date 1, he forgoes the gain M0 from exploiting the mispricing at date 0. Therefore, if Πwait M≥ 0 , all arbitrageurs prefer to defer their intervention.


Under the third scenario, the arbitrageur intervenes at date 0. Suppose the mispricing is expected to decrease κM1 < M0. In this case, the optimal strategy depends on the sensitivity of investor capital to interim perfor-mance. Waiting is optimal if an only if

Π Πwait or> do not wait

κ κϕM M M1 0 1> − .

We can rewrite this equation to show that waiting is preferable if the risk of forced liquidation is high enough. Where “high enough” is defined as the probability of forced liquidity ϕ being greater than ϕ,

ϕ κκ

=−M MM

0 1

1

.

(4.14)

Suppose bond A is even cheaper at date 1. In other words, we expect the mis-pricing to decline κ M M1 0< = . Even in this scenario, the arbitrageur may prefer to postpone intervention to date 1 if sensitivity to ϕ of the risk of early liquidation is great enough. If this risk is low instead ( )0 < <ϕ ϕ he intervenes at date 0. The relationship also shows that the arbitrageur’s choice will depend on the profits M1 from delayed intervention. Even if the liquidation risk ϕ is low, investors may prefer to postpone intervention if the profits from waiting are large enough.

A key insight from this model is that the arbitrage process can be ineffective in bringing prices back to fundamental values under certain circumstances. For example, the model showed that, when mispric-ing increases and an arbitrageur exhibits losses, capital constraints may inhibit him from executing market stabilizing strategies at a time when it is most needed in the market.

These results are very closely related to studies of market liquidity. When assets trade well below their fundamental values but there are no buyers of the asset, asset prices reflect a negative liquidity premium, or an illiquid-ity discount. The natural buyers of the assets may be prevented from buy-ing cheap assets because of capital constraints, so the market illiquidity persists.22


Liquidity premium according to the limits to arbitrage theory

To derive the liquidity premium, we extend our basic model to include nonspecialists, who know that they have less expertise than arbitrageurs. We assume that the nonspecialists are overly pessimistic as they value the bond at V − δ even though it will pay V for sure at date 2. We assume that δ > 0 and larger δ denotes the larger price discount expected by the nonspecialists.

Suppose that the mispricing M1 that occurs in the market for bond A at date 1 is due to a shock to the nonspecialists. When this shock occurs, the nonspecialists sell their holdings of bond A. We assume the aggregate sale from nonspecialists at date 1 to be

Supply from Non Specialists− = + − = −1 1 1 11 1δ δ

( ) .P V MA

We assume that the sell orders from the nonspecialists cause the mis-pricing of bond A and that it can only be corrected if purchases by arbitrageurs offset the supply. However, the arbitrageurs’ ability to do this is limited since they must allocate capital between different strate-gies (this is represented by intervention at date 0 and at date 1 in the model). Suppose that mispricing at date 0 is very pronounced. In this case, arbitrage capital will be massively invested to harness the date 0 mispricing, leaving little available to bet against the mispricing at date 1. Mispricing at date 1 will tend to persist and increase in the mis-pricing at date 0.

The probability φ(i) with which an arbitrageur would be forced to liq-uidate his position prematurely in the event that the mispricing increases at date 1 was defined in the section titled “Limited arbitrage due to risk of early liquidation.”23

To determine the equilibrium price of bond A at date 1 when a supply shock occurs, we resort to the basic economic principle that the equi-librium price occurs at the intersection of supply and the demand. The supply from the nonspecialists and arbitrageurs who are forced to liqui-date their position prematurely must be absorbed by those arbitrageurs who still have capital to invest at date 1 because they did not intervene at


date 0. Hence, the equilibrium price of asset A at date t = 1 is given by the condition that the sum of the nonspecialists’ and arbitrageurs’ sell orders should equal the arbitrageurs’ buy orders.

The formula for the market clearing condition under this model is

1 1 1 12

11

2− + + = −

δ δϕ ϕV PA .

(4.15)

This yields the equilibrium price of bond A at date t = 1:

p VA121

2= − +

δ ϕ ϕ .

(4.16)

We recognize the general form of this equation: market price equals the fundamental value V adjusted by a liquidity premium. The liquidity pre-

mium in this model is defined as [− +

δ ϕ ϕ1

22 ].

The liquidity premium

increases in the mispricing expectations of the nonspecialists and by the

sensitivity of capital under management.

Recall that the indifference condition ϕ defined in equation (4.13) pro-vides the relationship between the expected level of mispricing at date t=1 and the fraction of the arbitrageurs who intervene at date t=0. The fraction who intervene at date 1 determines the mispricing or liquidity premium in equation (4.15):

M121

2= +

δ ϕ ϕ .

(4.17)

In equilibrium, the market-clearing condition is given by the faction ϕ* of arbitrageurs intervening at date 0 and the date t=1 mispricing M*

1 that simultaneously solves equation (4.13) and equation (4.16). The equilibrium mispricing is shown graphically in Figure 4.3.

Application of the agency model to a funding crisis

The market-clearing condition derived in equation (4.14.) implied that a sufficient number of arbitrageurs exist who are able execute buy orders to offset the supply of the assets. In this section, we consider a special case of this model, in which the funding of these agents is exhausted so that there


is no demand for bond A. This will lead to a depletion of market liquidity for the asset when it is critical, which will in turn lead to a very sharp fall in asset prices. To capture this point in the model of the previous section, suppose that with very low probability, the arbitrageurs who saved their capital for intervention at date 1 are hit by an unexpected cut in financing at that date, possibly due to a credit crunch or to a capital loss in unrelated business. We call this a “crisis” state. Further assume that the crisis was unexpected. It does not affect the choice of a strategy at date 0, so that ϕ is determined by equation (4.13) as before. Hence, if the economy is not in crisis, the equilibrium mispricing is M1

*, as explained in the section titled “Liquidity premium according to the limits to arbitrage theory” and as shown graphically in Figure 4.3.

If a crisis does occur, however, the clearing condition defined in equation (4.14) is altered since there are no buyers to absorb the sell orders placed by other market participants. Formally, the new clearing condition is:

Supply from Non-Specialists + Supply from Liquidating Arbitrageurs = 0

1 1 1 12

01

2− + + =

δ δϕV PA ,

so the magnitude of the liquidity premium in crisis state is

M1crisis = +

δ ϕ1

212

. (4.18)

Indifference curve defined in equation (4.13)

M1

M1*

M0

2k

ϕ̂*

Indifference curve defined in equation (4.16)

ϕ̂

Figure 4.3 Mispricing and allocation of arbitrage capital.


Compared to its equilibrium value in normal times, the crisis state mis-pricing of the asset increases by

M M1 1 1crisis − = −* *( .)δ ϕ (4.19)

This implies that the illiquidity discount is higher and the price of the asset is lower during the funding crisis at date 1. The mispricing during the crisis will be more dramatic for larger values of δ.

The reason is that if the funding crisis prohibits the natural buyers, in this case arbitrageurs, from buying the asset, nonspecialists are the only ones left to hold the asset and in equilibrium must become net buyers. The nonspecialists’ valuation of the asset is V − δ. Hence, higher δ means that nonspecialists value the assets less, which would most likely occur dur-ing times of distress in the market. That is, the disappearance of funding capital at date 1 leads to a sharper fall in prices when δ becomes greater. This is illustrated in table 4.2, which compares the mispricing at date 1 in normal and crisis times.

We can see that the crisis is associated with a greater liquidity pre-mium, but that its magnitude is much greater if δ is higher, or, put dif-ferently, the liquidity premium increases in a lower reservation price of the nonspecialists. When δ is larger, the drying-up of the liquidity normally supplied by arbitrageurs has a more dramatic effect. In fact, the arbitrageurs who are forced to close their positions at date 1 help push the price even below the valuation of the nonspecialists, who in the crisis end up absorbing sell orders at sufficiently discounted prices of the security.

The size of the mispricing M1 reflects the price impact of market illiquidity.

The limits to arbitrage argument supports episodes of contagion across asset markets. In the event that the capital of arbitrageurs may

Table 4.2 Liquidity premium with and without a funding crisis

M1* Mcrisis

1 M Mcrisis1 1- *

δ = 0.9 1.29 1.55 0.26

δ = 0.1 2.8 10.27 7.47


be insufficient, since they draw from the same pool of capital to absorb shocks in different markets, a drop in their capital may force them to liquidate positions in other markets, affecting asset prices in multiple markets. We consider the mechanisms whereby the shock in one market affects other markets in more detail in chapter 6.

A mean-variance framework for pricing liquidity risk

The classical mean-variance framework assumes a perfectly liquid mar-ket, as explained in chapter 1. Market liquidity, however, is pivotal in real-world markets, and investors require compensation for bearing sys-tematic liquidity risk over and above the compensation for market risk. Market liquidity is also uncertain and varies over time. In this section, we discuss an expansion of the classic mean-variance framework developed by Professors Viral Acharya and Lasse Pedersen that includes market liquidity risk.24

The liquidity-adjusted capital asset pricing model (LCAPM) provides us with the tools needed to explain how liquidity risk and commonal-ity in liquidity affect asset prices. In particular, a security that has high average illiquidity also tends to have high commonality in liquidity, high return sensitivity to market liquidity and high liquidity sensitivity to mar-ket returns.

The traditional capital asset pricing model (CAPM) assumes that finan-cial markets are frictionless and that trades are executed at no cost. The Acharya-Pedersen pricing model incorporates market liquidity as stochas-tic trading costs. Stochastic trading costs are representative of real-world markets in which market liquidity is uncertain and varies over time. The other assumptions of the traditional CAPM—risk adverse investors max-imizing their expected utility under a wealth constraint—are kept intact, so LCAPM reduces to the traditional CAPM when trading costs are zero. LCAPM complements the other theoretical pricing models with constant trading costs, for example, the search and bargaining model discussed in the section titled “Liquidity premium of a search-and-bargaining model,” or the simple bid-ask spread model with exogenous trading costs dis-cussed in the section titled “Exogenous (‘add-on’) cost of trading.”


Liquidity-adjusted capital asset pricing model

The key insight of the traditional CAPM is that investors earn a higher expected return for greater systematic market risk that cannot be diversi-fied by any single investor.25 More formally, an investor earns a net return from a position in a security j:

E r r E r rj fj

M f( ) = + ( ) − β . (4.20)

The correlation coefficient or beta βj is defined as

β j

j M

M

r rr

=( )

( )cov ,

var. (4.21)

The beta tells us how the return on the security and the return on the market move together. In this model, we assume that agents can borrow and lend at the risk-free interest rate rf, which is determined exogenously.

Real-world financial markets suffer illiquidity. Furthermore, liquidity is uncertain and it varies over time. To capture the nature of liquidity, we can model it as a stochastic process. Assume that agents can buy at the market-clearing price pt

i, but must sell at p ct

iti− . In this model, liquidity

is a random variable, cti, which represents the per-security cost of trans-

acting. The aggregate market measure of illiquidity is cM. Short selling is not allowed. To derive the liquidity-adjusted version of the CAPM, we express the net return in terms of the gross return Rj and a transaction cost cj:

r R cj j j= − .

The liquidity-adjusted CAPM expressed in terms of the gross return and transaction cost is as follows:

E R c r E R c rj j fj

M M f−( ) = + −( ) − β , (4.22)

where βj is related to the respective risk covariance,

β j

j M

M

j j M M

M

r rr

R c R cr

=( )

( ) =− −( )

( )cov ,

varcov ,

var.


Denote the risk premium on the market portfolio as λ = −( ) −E R c rM M f. Equivalently, we can express the conditional expected gross return in terms of the market risk premium as

E R r E cR R

rc c

r

R

j f jj M

M

j M

M( ) ( )cov ,

varcov ,

var

cov

= + +( )

( ) +( )( )

−

λ λ

λjj M

M

j M

M

cr

c Rr

,var

cov ,var

.( )

( ) −( )

( )λ

(4.23)

This equation is the liquidity-adjusted CAPM developed by Acharya and Pedersen. This formulation states that the required excess return is the expected level of liquidity plus four betas multiplied by the market risk premium. The four betas, defined below, determine the liquidity risk and they depend on the comovement of the security’s payoff and liquidity risks with the return and liquidity risk of the market portfolio:

β j

j M

M

R Rr

1=( )

( )cov ,

var,

β j

j M

M

c cr

2=( )

( )cov

var,

,

β j

j M

M

R cr

3=( )

( )cov ,

var,

β j

j M

M

c Rr

4=( )

( )cov

var.

,

The factor β j1 denotes the market beta of the traditional CAPM, but

the denominator is adjusted for a term related to trading costs. In the absence of transaction costs, this reduces to the market beta of the tra-ditional CAPM. Factor β j

2 represents the commonality in liquidity that is the liquidity risk arising from the comovement of security liquidity with market liquidity. According to the model, and supported by empiri-cal results, investors require a return premium for securities that become illiquid when the markets in general become illiquid.


The factor β j3 captures the liquidity risk arising from the comovement

of the security return and market liquidity, and the factor β j4 measures

the comovement between the security’s liquidity and the gross market return.

Economic interpretation of liquidity risk premium

The liquidity-adjusted CAPM provides a framework for identifying the channels through which liquidity risk is priced. For the purposes of this discussion, we rewrite equation (4.23) according to a simplification from Foucault: 26

E R r E cj f jj j j j( ) = + ( )+ + − −( )λβ λ β β β1 2 3 4 . (4.24)

We can further simplify the pricing equation to separate market risk, cap-tured by β j

1, and liquidity risk, captured by β jLIQ,

E R r E cj f jj j( ) = + ( )+ +λβ λβ1 LIQ, (4.25)

where β β β βj j j jLIQ= − −( )2 3 4 denotes the pricing effect of liquidity risks as

a linear combination of three liquidity betas. Note that the liquidity level premium and the liquidity risk premium are collectively referred to as the liquidity premium in this model. Each of the three risk betas identifies a channel through which liquidity risk is priced.

The risk premium, β2, captures the comovement between liquidity of the security and liquidity of the overall market. A high β2 means that the security would typically become illiquid when the overall market becomes illiquid. The average investors will require a higher return on these securities as compensation for this component of liquidity risk. The risk premium β2 therefore captures the pricing effect of the commonality in liquidity or more generally the time-varying common factor in liquid-ity. This channel shows that it is not just the liquidity of the individual asset that is important, but that shocks that affect the overall market liquidity also affect asset pricing. The capital constraints discussed under the Limits to Arbitrage provide one explanation for the commonality in liquidity. Other studies also support the existence of the commonality in


liquidity. For example, some studies explain the commonality in liquidity as originating from the inventory risk of market intermediaries or from common market factors such as asymmetric information between market participants.27

The risk premium β j3 measures the effect of the liquidity risk on

expected return arising from the sensitivity of the security return and market liquidity. The model shows that a higher value for β j

3 reduces the expected return. A high β j

3 implies that the security does well when the market liquidity decreases. In other words, securities with a high β j

3 offer a hedge against a drop in market-wide liquidity. The asset-pricing effect of this liquidity channel has also been studied extensively by others.28 An interesting empirical example of the detrimental effects of this compo-nent of liquidity can be found in the hedge fund LTCM. The fund imple-mented a strategy whereby they purchased less liquid securities and sold more liquid securities, essentially profiting from the expected return dif-ferential of the portfolio. However, when market liquidity deteriorated due to the Russian debt crisis in 1998, LTCM portfolio value deteriorated due to its sensitivity to the liquidity of the debt market.

The risk premium β j4 measures the effect of the liquidity risk on expected

return arising from the security’s liquidity and the gross market return. Securities with a high β j

4 remain liquid when the market goes down. The model shows that this risk premium is negatively related to expected return since investors value a security that can easily be sold in a down market and are willing to accept a lower relative return. When an investor holds securities that are illiquid at a time when the market is down, losses escalate because of losses on illiquid securities. This is well recognized by practitioners: “[b]because there are so few buyers, you’re forced to sell at a discount that is both huge and unpredictable.”29 The pricing implications of β j

4 are supported by the research of Markus Brunnermeier from Princeton University and Lasse Pedersen from New York University.30 Brunnermeier and Pedersen showed that liquidity risk is realized through this channel when investors simultaneously hit their funding constraints and are forced to liquidate their positions in a distressed market. A related example of liquidity risk being realized through this channel is asset fire sales, which we discuss in more detail in chapter 6.


Discussion of selective empirical results

Empirical studies agree that commonality in liquidity risk, β j2, is the least

important component of the liquidity risk premium, while the risk pre-mium related to the comovement between asset illiquidity and market return β j

4 is the most important.31

Professors Björn Hagströmer, Björn Hansson, and Birger Nilsson used a comprehensive data set covering over 80 years of NYSE trading between 1927 and 2010 to study the dynamics of the liquidity premium over time.32 They decomposed the total liquidity premium into a compensa-tion for the level of liquidity and a compensation for liquidity risk. The level premium is the ratio of the expected liquidity cost and the expected holding period. The liquidity risk premium is related to the sum of the three liquidity betas β j

LIQ of the LCAPM defined in equation (4.24).33 The results using the NYSE data showed that the liquidity premium varies substantially over time, with peaks in downturns and crises, but with no general tendency to decrease over time.

The professors estimated a liquidity level premium between 1.25 and 1.28 percent per year, and an aggregate liquidity risk premium between 0.46 and 0.83 percent per year. The summation of these two effects gives us the total liquidity premium of between 1.74 and 2.08 percent per year. Both the liquidity level premium and the liquidity risk premium show considerable variation over time. The estimates of each by Hagströmer, Hansson, and Nilsson are shown in Figure 4.4.

The time variation of the liquidity level premium (LP), shown in black, and the liquidity risk premium (RP), shown in gray, is evident from Figure 4.4. The relative importance of the liquidity level premium is consis-tent throughout the sample period. The liquidity risk premium (RP) exhib-its significant time variation, with peaks during crisis periods. For example, consider the peak during the Great Depression of the 1930s and then again during World War II. Another interesting observation from this data is the high liquidity risk during these earlier periods compared to the more recent 2007–2008 crisis period. Since the 1970s, the magnitude of the liquidity level premium has increased relative to the liquidity risk premium.

During the period 1965 to 1975, the liquidity risk premium appeared to follow an increasing trend, and since then it has gradually been


decreasing. Well-known periods of distress, including the oil crises of 1973 and 1979, the October 1987 crash, the LTCM bankruptcy in 1998, and the Lehman Brothers bankruptcy in 2008, are marked by peaks and high volatility in liquidity risk.

To explore the liquidity-adjusted CAPM in practice, you have to choose an empirical liquidity measure. Not all liquidity measures are the same, so using a single measure in the model may raise concerns of whether the results are driven by systematic but measure-specific components or by systematic and common components of measure liquidity.34 If we assume that multiple liquidity measures capture the systematic liquidity, then our choice of measure is not that critical. However, this becomes a question of which empirical liquidity measure to choose, but it does not obviate the theoretical insights afforded by the LCAPM. The original model was illustrated using the Amihud measure. It shows that there exists a system-atic common component across eight different liquidity measures. It is the systematic common component that is priced, as shown empirically using security market data for the period.35

In this approach, the level of liquidity at a given point in time and the sources of liquidity risk, that is, security liquidity and market liquidity, are exogenous. The model also does not explain why there is commonality

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erce

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Liquidity PremiumRisk Premium

Figure 4.4 The liquidity level premium and the liquidity risk premium.


in illiquidity across stocks or between illiquidity and security returns. In chapter 6, we describe how changes in illiquidity may occur and how commonality liquidity can emerge.

Implications for asset pricing and risk management

Each of the frameworks we discussed in this chapter highlights a differ-ent aspect of market liquidity and provides insight into the mechanisms whereby market liquidity affects asset prices. For example, bid-ask spread, the Amihud measure, and the Kyle market impact measures employ principles of market microstructure to measure market liquidity. These models assume that market liquidity can be captured sufficiently as an exogenously added transaction cost. While these measures are easy to implement, and are useful diagnostic tools, they have limited value in pricing securities before the actual trade have been executed.

The search-and-bargaining framework provides insight into the driv-ing factors of market liquidity in an intermediated market setting. The framework furthers our understanding of market liquidity that arises endogenously as part of the trading process and the institutional arrange-ments of trade. The Shleifer-Vishny agency model highlights the impor-tance of capital for trading and quantifies the liquidity premium in the event that capital is constrained. The dynamic and uncertain nature of market liquidity is succinctly captured and quantified in the LCAPM using a stochastic process to represent liquidity.

Appendix Chapter 4

Derivation of the Search-and-Bargaining Model

We derive the bid and ask prices in the search-and-bargain model dis-cussed in the section titled “Liquidity premium of a search-and-bargaining model.”36 The full set of investor types in each period is Ι = { , , , },π π π πh

ohno

lo

lno

where the subscript “l” and “h” indicates the investor’s liquidity state, low or high, and the superscript “o” and “no” distinguishes respectively between owners and nonowners. The per-capita supply of the consol bond is denoted by q. In each period, the fraction of investors willing to buy the security isπ ψπ ψ πb l

nohno= + −( )1 , and the fraction willing to sell

the security is π ψπ ψ πs ho

lo= + −( )1 .

Consider the case in which we have πb > πs. There is excess demand for the security, so its price is determined by the maximum value that buyers place on it.

Let πh be the steady-state fraction of high rollers and πl be the steady-state fraction of low rollers. It follows, then, that π π π π π πh l h

ohno

lo

lno+ = + + + = 1.

Next, posit that in each period a fraction ψ of high rollers becomes low rollers, and vice versa. Hence,

π ψ π ψπ ψ π ψ πh h l h h= −( ) + = −( ) + −1 1 1( ).

Solving this equation for the fraction of high rollers yields πh = 1

2.

By definition, π π πh ho

hno= + and π π πl l

olno= + . Moreover, all shares

are necessarily owned either by high rollers or by low rollers so that q l

oho= +π π . We have the following set of equations:

π π πh ho

hno= + = 1

2,


π π πl lo

lno= + = 1

2, and

π πlo

ho q+ = .

Using these equations, after some straightforward steps, one obtains

π πb s q= + −

12

.

So πb > πs, if and only, if q < 12

.

Let a be the maximum price that an investor is willing to pay and b be the minimum price that a seller is willing to accept from a dealer. We also assume that dealers cannot hold inventories: their aggregate inventory at the end of each period must be zero, and dealers with long positions sell the asset to those with short positions. We assume that these interdealer transactions take place at price:

µ = +a b2

.

At any ask price a less than a the investor demand for the security exceeds its supply, expressed mathematically as πb > πs, if a a= , buyers are indif-

ferent. We assume that they choose to buy with probability ρ φ π

πb s

b

= . In

this way, the number of buy orders received by dealers in the aggregate is just equal to the number of sell orders, so their aggregate inventory is zero, as required. Hence, the ask price set by dealers a equals buyers’ res-ervation price a .

Dealers’ bid price cannot be determined by the same reasoning, because at any bid price below μ, there is no excess supply of the security. Market makers and sellers bargain over the bid price, producing a bid price that is the average of the dealers’ and sellers’ valuations, weighted by their respective bargaining power:

b zb z= + −( )1 µ,

with 0 ≤ z ≤ 1. As dealers’ bargaining power z increases, they extract a larger surplus from sellers. At the limit, for z = 1, the surplus left to sellers is zero.


To obtain the equilibrium ask and bid prices, we must compute a and b . We first compute the discounted value of the future stream of cash flows that each type of investor expects to receive just after trading in a given period.

Let Vjk be the discounted value for a trader with valuation j h l∈{ }, and

type k o no∈{ }, . Next, consider the high-valuation nonowner who con-tacts a dealer: he buys the security if and only if

V a Vho

hno− ≥ ,

since otherwise he is better off staying a nonowner. Similarly, a low-valu-ation owner will sell the security to a market maker if and only if

V b Vlno

lo+ ≥ .

Thus, a V V Vho

hno

h= − = n and b V V Vlo

lno

l= − = n . To determine nVh and nVl , we first calculate Vj

k for j h l∈{ }, and k o no∈{ }, . The value placed by a high-roller owner on the security is

Vr

Vr

Vr

V brh

o ho

lo

lo

=+

+−( )

++

−( )+

++( )

+1

11

111 1

ψ ψ φ ψφ. (A4.1)

To understand this expression, observe that a high-roller owner always receives $1 with certainty at the beginning of the next period, which explains the first term. The last three terms are simply the weighted average of the discounted cash flows for the investors in each of the possible states at the end of the next period, the weights being the respective probabilities. With probability (1−ψ), the investor remains a high-roller owner and therefore values the discounted cash flow of the asset at Vh

o. This explains the second term in the equation. With prob-ability ψ φ1−( ), he turns into a low-roller owner who does not manage to sell to a dealer, and thus ends up valuing it at Vl

o in the subsequent period. This explains the third term. Finally, with probability ψφ , the investor becomes a low roller who does manage to resell the security at price b in the next period. In this state, the investor receives b, but also keeps the option of buying at some point in the future. The value of this option is Vl

o. This state is captured by the last term.


Proceeding in the same way, we obtain the discounted value of future cash flows for a high roller who does not own the asset:

VV

rV

rV arh

no lno b

hno b

ho

=+

+−( ) −( )

++

−( ) +( )+

ψ1

1 11

11

ψ ρ ψ ρ. (A4.2)

The first term corresponds to the state in which the investor’s valuation drops, so in the next period he wants to buy the asset. The second term refers to the state in which his valuation stays high, but he does not man-age to buy the security from a dealer, and the last term to the situation in which the investor does buy the asset from a dealer (at price a with probability ρb) and therefore owns the asset at the end of the next period. Following the same reasoning, we get

V cr

Vr

Vr

V brl

o ho

lo

lno

= −( )+

++

+−( ) −( )

++

−( ) +( )+

11 1

1 11

11

ψ ψ φ ψ φ. (A4.3)

and

VVr

Vr

V arl

no lno b

hno b

ho

=−( )

++

−( )+

++( )

+1

11

1 1ψ ψ ρ ψρ

. (A4.4)

From equations (A4.1), (A4.2), (A4.3), and (A4.4) we obtain

∆∆ ∆

VV b a V a

rhl

bh=

+ −( ) + + −( )( ) − −( ) −( ) −( )+( ) − −( )

1 1 1 11 1

ψ φ ψ ψ φ ψ φ ρψ 11−( )φ

.

Recalling that a Vh= ∆ , this expression can be rewritten as

∆∆

VV b a

rhl=

+ −( ) + + −( )( )+( ) − −( ) −( )

1 1 11 1 1

ψ φ ψ ψ φψ φ

. (A4.5)

Proceeding in the same way, we obtain

∆∆

Vc V b a

rlh=

−( )+ −( ) + −( ) ++( ) − −( ) −( )

1 1 11 1 1

ψ φ ψ φ ψφψ φ

.


Hence,

∆ ∆V Vc S

rh l− =−( )+ −( )

+( ) − −( ) −( )1 1 2

1 1 1ψ φ

ψ φ, (A4.6)

where S = a−b is the bid-ask spread charged by dealers.Now, recalling that

a Vh= ∆ (A4.7)

and

b b= + −( )z 1 z µ (A4.8)

so µ = + =+a b V Vh l

2 2∆ ∆ , we deduce from equations (A4.7) and (A4.8)

that

∆ ∆V Va b

zSzh l− =

−( )+

=+

21

21

. (A4.9)

Substituting ∆ ∆V Vh l− , defined in equation (A4.6), into equation (A4.9) and solving for S, we get

Sz c

r z=

−( )+( ) − −( ) −( )

12 2 1 2 1ψ ψ φ

, (A4.10)

which is the expression shown in equation (4.12) and discussed in detail in the titled “Liquidity premium of a search-and-bargaining model.” This expression shows that the bid-ask spread increases or, equivalently, the market liquidity decreases if the dealer’s bargaining power z and or hold-ing cost c increases.

Introduction

In 1959, Professor Lawrence Fisher1 presented a hypothesis about the determinants of the risk premium on corporate bonds.2 Fisher showed that the average risk premium, defined as the yield differential between a corporate bond and the risk-free rate, depends on two factors. The first factor reflects the default risk or creditworthiness of the issuer. It captures the basic idea that the lender will get their money back. Incidentally, this factor has dominated our thinking about bonds in general and corpo-rate bonds in particular. But Fisher also considered the “marketability” or liquidity of the bond, defined as the market value of the firm’s out-standing bonds traded in the secondary market, to contribute to the risk premium.3 If Fisher introduced the idea of “marketability” in the middle of the last century, why have we for the most part ignored bond market liquidity?

Our lopsided attention to credit risk can be traced back to three fac-tors. First, the risk premium usually dominates during good times, while market liquidity usually takes center stage during times of financial dis-tress. For example, liquidity of the bond market was of particular concern after the 1929 railroad crash, in the fall of 1998 after Long Term Capital Management (LTCM) was liquidated, and most recently in the aftermath of Lehman Brothers’ default in September 2008. During September 2008, the yield spread between ten-year government agency bonds and similar-duration Treasury bonds was as much as 170 basis points, which was con-siderably wider than a normal spread of 10 to 30 basis points. Such wide spreads for the highest-quality borrowers pointed to illiquidity rather

5 Stories of Liquidity and Credit


fundamental problems.4 The near-collapse of the commercial paper and repurchase markets effectively sidelined natural buyers of risky debt who relied on these markets to finance their purchases.

The second factor is the lack of generally accepted asset pricing mod-els that simultaneously account for the effects of credit risk and market liquidity. The workhorse model for the pricing of defaultable securi-ties is the seminal Merton model, named after the famous Nobel laure-ate Dr. Robert Merton. Merton solved the problem of credit risk, or the probability that a firm will be unable to fulfill its obligations, assuming a market without impediments to trade.5 Brokerage firm analysts and inves-tors employ the model to determine the company’s ability to service its financial obligations. But credit spreads obtained from the Merton model typically do not match the market-observed spreads across ratings cat-egories.6 In this discussion, we develop the argument that liquidity could be the missing ingredient in this standard pricing model.

The third impediment to the analysis of liquidity is that historically we lacked credible information on the majority of fixed-income trades. Unlike stocks, fixed-income securities such as corporate bonds, collat-eralized debt obligations (CDOs), and credit default swaps do not trade in a centralized market, but rather in the more opaque over-the-counter (OTC) market. Unlike centralized markets, information on traded prices and volumes is not readily available in an OTC market structure, as dis-cussed in chapter 3.

The importance of information is, however, well recognized by aca-demics, regulators, and fixed-income traders. Reflecting on liquidity con-cerns in corporate bonds, the National Association of Security Dealers (NASD) instituted Trade Reporting and Compliance Engine (TRACE), which captures and disseminates consolidated information on second-ary market transactions, such as prices and volumes, in publicly traded TRACE-eligible securities (investment-grade, high-yield, and convertible corporate debt).7

In principle, the liquidity premium should reflect an investor’s percep-tion of conditions in the secondary markets, and the probability of having to take a large price discount at the point of sale. In reality, bond liquidity is influenced by a range of factors. These include long-standing structural

Stories of Liquidity and Credit 117

factors, for example, allocation of foreign exchange reserves in some emerging markets, shifts in real money investor preferences, and regula-tory requirements, as well as cyclical factors such as heightened risk-taking prompted by the global low interest rate environment and manifested by elevated appetite for credit and liquidity risks in the so-called “search for yield.” Monitoring and understanding the drivers of liquidity risk premia are therefore important because low bond liquidity premia may disguise an underlying fragility of the financial system as a whole.

The 2007–2008 financial crisis and the sovereign debt crisis in Europe and Argentina demonstrated the importance of the interaction between default and liquidity in financial markets. Another conundrum for simple discounted cash flow models of bond pricing is the opaque link between the liquidity premium and the default premium. For example, during the 2007–2008 financial crisis, deterioration in debt market liquidity caused severe financing difficulties for many financial firms, which in turn exac-erbated their credit risk. Even during times of normal market operations, empirical results show that the time variation in bond indices for dif-ferent ratings exceeded the credit factor and are related to differences in aggregate market liquidity. For two bonds in the same ratings category, a difference in their market liquidity could result in a difference in their yield spreads.8 Some asset pricing models treat the default and liquid-ity premia as independent, thus ignoring the interactions between credit risk and market liquidity. Empirical studies demonstrate that interactions between market liquidity and credit risk could affect a firm’s cost of capi-tal through several channels. We discuss the implications of refinancing debt in an illiquid market in this chapter. Understanding the portion of corporate yield spread attributable to default risk versus market liquidity is critical from a corporate finance perspective because it directly affects capital structure issues such as the timing of debt issuances, but it is also of fundamental importance from an investment and risk management perspective.

Considering the nature of bond markets (bonds are “real cash”), we can make a compelling argument that theories of bond market liquidity should straddle trading, regulatory policies, and balance sheet strategies, as well as macroeconomic factors such a monetary policy (while similar


influences are observed in the equity or derivative markets, the links are more indirect). The frameworks we discuss approach the problem of bond market liquidity, and more specifically the interaction with credit, from these different perspectives. Our goal is not to offer a comprehen-sive treatment of the vast amount of research that has been published since the recent financial crisis, but to provide the reader with different perspectives that will enhance our understanding of market liquidity.9

We start our discussion with a review of the literature on diagnostic tools of bond market liquidity and then investigate the underlying mar-ket mechanisms and corporate strategies that could potentially reduce bond market liquidity. We discuss two general frameworks of quantifying the bond liquidity premium that take credit risk into account. We focus on corporate bonds as quintessential securities to examine interactions between liquidity and credit risk factors, but the principles we discuss apply equally to other fixed-income securities, such as CDOs or asset-backed securities (ABSs) with default risks. According to the Securities Industry and Financial Markets Association (SIFMA), the aggregate cor-porate US debt outstanding as of September 2014 was approximately $40 trillion. As of the second quarter of 2014, US corporate bond issuance totaled over $410 billion, exceeded only by the issuance of US Treasury debt.

Diagnostic tools of bond market liquidity

Investors care about liquidity in corporate bonds. There is also consider-able variation in the credit quality and liquidity in this market, both over time and across bonds. Prices from standard discounted cash flow models do not necessarily match traded bond prices because, for the same prom-ised cash flows, less liquid bonds will trade less frequently, realize lower prices, and exhibit higher yield spreads. Consider the yield differential between US Treasury and comparable maturity AAA corporate bonds. Between 1926 and 2008, investors paid on average 72 basis points per year for the liquidity and safety attributes of Treasuries relative to cor-porate bonds. Approximately 46 basis points of the 72 basis point spread account for the liquidity benefits of Treasuries relative to corporate bonds,


and 26 basis points account for their relative safety.10 As we established in chapter 4, investors demand a liquidity premium for illiquid securities. Liquidity cost inhibits the ease and frequency of trading. Because inves-tors cannot continuously hedge their risk, they demand an ex-ante risk premium by lowering security prices.

The OTC market, in which the majority of corporate bonds are being traded, is fundamentally different from the exchange-traded market, compelling us to go beyond standard market microstructure transaction-based measures frequently used in exchange-traded markets to monitor bond market liquidity. One such measure is the bid-ask spread, which could range from as little as 3 basis points to as much as 150 basis points, according to some studies.11 Empirical results show that liquidity in cor-porate bonds is significantly greater than what can be explained by bid-ask spreads alone.12 Even though it is often referenced, the bid-ask spread is generally only available for relatively larger bond issuances, and it is a one-dimensional measure that does not reflect aspects of market liquid-ity such as the market depth, in other words, the size of the transaction that can be absorbed without affecting the price, or the speed with which orders can be executed. The bid-ask spread typically does not sufficiently capture important drivers of market liquidity in the OTC market, such as search frictions, bargaining power, or inventory holding costs, and it is therefore not a well-suited metric in the OTC market.

Despite these limitations, the bid-ask spread is still being used due to a lack of consensus on how to price and monitor liquidity in corporate bonds. In the next three sections, we discuss some alternative diagnostic tools that risk managers, portfolio managers, and regulators can use to monitor bond market liquidity.

Corporate bond-credit default swap basis

The bond-credit default swap basis compares the yield spread of a cor-porate bond over the risk-free interest rate and the corresponding credit default swap premia on the same reference entity, same maturity, same seniority, and same currency. Corporate bonds and the corresponding credit default swaps, or reference basket of corporate bonds, carry the


same credit risk, so the basis should reflect the relative compensation required by investors for bearing other noncredit risks, such as the liquid-ity premium priced in the corporate bond market.13 A common measure of the risk-free benchmark is interest rate swaps, but some also use the Treasury yield with a maturity matching that of the corporate bond.

No-arbitrage requirements stipulate that whenever the bond-credit default swap basis is sufficiently different from zero, it is theoretically pos-sible to implement a basis trade, selling (buying) credit risk in the bond market and buying (selling) credit risk in the derivative market using a credit default swap. For this relationship to hold or, put differently, for the basis trading strategy to be profitable, markets should be relatively liquid with narrow bid-ask spreads, funding for bond purchases should be unconstrained, and the interbank market should function efficiently.14 In general this arbitrage is not perfect due to technical reasons, such as the cheapest-to-deliver option of the credit default swap and the practical challenges that are involved in short-selling bonds, factors that tend to render the basis slightly positive during liquid markets.15

Academic research, supported by empirical results, shows that a reason for the divergence in the bond-credit default swap basis is illi-quidity in the corporate bond market.16 The bond-credit default swap basis is used to monitor the liquidity premium in the corporate bond market. The relative simplicity of this approach is appealing. Because it ignores other factors that can also affect the basis, this approach can lead to a biased estimate of liquidity. For example, when bank liquidity is scarce, credit default swap spreads may also include an allowance for counterparty credit risk, and so it is not necessarily a clean measure. Credit default swaps may also have embedded liquidity risk that biases the default component, which in turn leads to a bias in the measure of the liquidity premium itself.17

Liquidity measure based on return covariance: the modified Roll measure

The proponents of the random walk argue that changes in the price of the current transaction will not be influenced by the sequence of preceding


price changes. Randomness of successive price changes is, however, not reflected in real-world transactions. In 1966, the hedge fund manager and New York Times best-selling author Victor Niederhoffer, studying the ticker tape of traded prices as published by Francis Emory Fitch, Inc., observed that successive price changes show considerable dependence.18 He pro-vided empirical evidence of nonrandomness in stock prices and further observed that there was a general tendency for price changes to reverse. In the language of an order book, price changes tend to reverse when order flow is balanced. In other words, trades on the bid side are followed by trades on the ask side of the market. The relevance of Niederhoffer’s obser-vation for bond markets lies in his recognition of the link between price reversals and market liquidity. Price reversals give rise to transitory effects in traded prices. Niederhoffer’s insight that the magnitude of transitory price movements reflects the degree of market liquidity forms the basis for the modified Roll measure. The modified Roll measure provides a simple framework of market liquidity. Assume that the fundamental value Vt fol-lows a random walk. Express the observed transaction price as

P V ut t t= + . (5.1)

The second component, ut, represents the transitory component, and it is assumed to be uncorrelated with the fundamental value. In this frame-work, the magnitude of the transitory price component ut characterizes market liquidity and more specifically in this model it refers to the level of illiquidity in the market. We can extract the transitory component of the transaction prices by computing the covariance of observed changes in transaction prices:

γ = − ( )−cov ∆ ∆P Pt t, .1 (5.2)

Because transitory price movements lead to negatively serially corre-lated price changes, the negative of the autocovariance in relative price changes, denoted by γ, gives a meaningful measure of illiquidity. A greater numerical value of γ indicates a less liquid market. In general, γ depends on the horizon over which price changes are measured. The horizon effect is important because γ measured over different horizons may capture dif-ferent aspects of the transitory price components. For example, using


trade-by-trade prices or end-of-day prices in estimating γ captures more of the high-frequency components in transitory price movements.

The measure can be recognized as an extension of the Roll measure we discussed in chapter 4, but unlike the Roll measure, it does not restrict the covariance to the effective bid-ask spread. The relationship between the Roll measure and the modified Roll measure is

SRoll = 2 γ . (5.3)

Equation (5.3) quantifies the underestimation of liquidity using the Roll measure. Because the modified version in equation (5.2) is less restrictive in its assumptions than the original Roll measure, it captures the broader impact of liquidity on prices beyond the effect of bid-ask spread by rely-ing on transaction prices. Another advantage of the modified Roll mea-sure is that it does not rely on any particular bond-pricing model.

Professor Jack Bao of The Ohio State University, and Professors Jun Pan and Jiang Wang of Massachusetts Institute of Technology employed the modified Roll measure to examine the pricing implications of market liquidity on corporate bonds spreads. Their study quantified the relative importance of liquidity and credit using transaction-level data from 2003 through 2009.19 Using the modified Roll measure as a diagnostic tool, Bao, Pan, and Wang provide valuable insight into the dynamics of bond market liquidity and in particular the interaction between liquidity and credit. The empirical results explain the cross-sectional variation of liquidity at the bond level, after controlling for credit risk using either credit default swap spreads or ratings as a proxy for credit risk. During normal market conditions, liquidity and credit are equally important drivers of high-rated yield spreads. However, during the 2008 crisis, liquidity was by far the most important factor in explaining the monthly changes in the US aggregate yield spreads of high-rated bonds (AAA through A). The results show that for two bonds in the same rating category, a one standard deviation difference in the market liquidity of these bond leads to a difference of up to 65 basis points in their yield spreads.

Bao, Pan, and Wang’s results show that the modified Roll measure is robust and that magnitude and statistical significance of this measure


persist even after controlling for proxies of bond liquidity such as the average trade size or the age of the bond.

Regression model using liquidity proxies

Why limit ourselves to a single liquidity measure? Given the multifaceted nature of bond market liquidity, we could combine various liquidity mea-sures as explanatory variables in a linear regression model. The frame-work of a regression model also allows us to separately control for credit risk. Opponents of regression models can rightly point to two potential problems with this framework. First, there is the issue of multicollinear-ity when using somewhat related measures as independent variables. However, according to comprehensive empirical research by Nils Friewald et al.,20 the various liquidity proxies measure somewhat different aspects of liquidity empirically, rendering the issues of multicollinearity less severe in this application. The second objection is that the simplicity of a linear model does not capture the nonlinear tail dependencies typically present during times of financial distress. Regression models can overcome this limitation by analyzing periods of financial distress and normal periods separately.21 Within the framework of regression models, the possible variations are endless. We base our discussion of the prominent features of regression models on the 2012 study done by Professors Friewald and Rainer Jankowitsch of the Vienna University of Economics and Business, and Professor Marti Subrahmanyam of New York University.22

Friewald, Jankowitsch, and Subrahmanyam relied on a panel regression to analyze changes in bond yield spread as the dependent variable, and trading activity and liquidity measures as the explanatory variables:23

∆∆

( )(

,Yield Spread Yield Sp

i t

oa= + β1 rread

Trading Activity Variabl

Liquidi

)

( )

(

,

,

i t

i tes−

+

+

1

2

3

ββ

∆

∆ tty Measures

Rating Dummies

)

( ) .,

, ,

i t

i t i t+ +β ε4∆ (5.4)

The rating class dummies are added to the model to control for effects related to credit risk. The first lag of the yield spread change represents an


observed autoregressive component in spreads. Trading activity variables include metrics such as the number of trades, trade volume, and trading interval. Higher trading activity is usually associated with more liquid markets. Longer trade intervals indicate less trading activity and there-fore lower liquidity.

Liquidity measures include the Amihud and Roll measures discussed in chapter 4, as well as a price dispersion and zero-return measure.24 Empirical results suggest that among the liquidity measures, the Amihud and price dispersion measures are statistically more significant. Among trading activity variables, changes in the volume and trading interval are statistically more significant than the number of trades. Indirect prox-ies of market liquidity based on bond characteristics such as the coupon, age, issued amount, and bond covenants could also be included in the model.25 In general, the liquidity of a particular bond decreases with the bond’s age and maturity, but increases with its issuance size.26

Friewald et al. applied this regression model to a comprehensive set of corporate bond trading data between 2004 and 2008. Liquidity effects accounted for approximately 14 percent of the explained market-wide changes in corporate bond yield spreads. The Amihud measure was eco-nomically the most important explanatory variable among the variables considered. The study also found that relying on simple bond characteris-tics and trading activity variables captures idiosyncratic information, but if used alone, these measures will not sufficiently capture market liquidity. Measures such as the Amihud and Roll measures that rely on traded prices and volumes use trading information more effectively, and the Friewald et al. study found them to be superior measures of market liquidity.

Regression-based approaches are generally useful in confirming the existence of a liquidity premium that varies over time, and the popularity of these methods in empirical work lies in their simplicity. However, a direct link between regression betas and bond prices has not been estab-lished. A further limitation of the regression approach is that it requires a large number of observed trades that may not be readily observable. Implicit in the regression model is the assumption of a linear relationship between credit and liquidity, which may not necessarily be true in real-word market transactions.


Empirical evidence of bond market liquidity and linkages to credit risk

Improving the existing asset pricing models of defaultable securities natu-rally forces us to develop a deeper understanding of the relative importance of liquidity and credit risk and how their importance varies with market conditions. It is informative to use the yield spread of corporate bonds over comparable maturity Treasury bonds as the discussion metric in this section.

An ignorant observer may assume that the yield spread represents default risk and accordingly draw conclusions regarding default probabil-ities from spreads. But these estimated default probabilities do not agree with empirical data. The yield spreads are typically wider than justified by historical default losses and may not be as informative about default risk as is often assumed. While the concept of a spread is easily described, the historical properties of corporate bond markets show that spreads are compromised. Bond yield spreads also reflect noncredit factors such as liquidity and to some extent also macroeconomic activity.27

Another key observation in the liquidity-credit story is that bonds have finite maturity, giving rise to the notion of a term structure of bond yields. The term structure simply means that bonds with different maturities can have different yields depending on the shape of the term structure. For example, if the yield curve is upward sloping, the yield on a short-term bond will be lower than the yield on a long-term bond. The Nobel laureate Robert Merton related the shape of the term structure to the probability of default of the issuer: high-grade corporate issuers face upward-sloping credit yield curves, while speculative-grade firms’ credit yield curves are downward sloping or hump-shaped (i.e., mostly downward sloping).28

An empirical study of the intricate dynamics between market liquid-ity and default risk was undertaken by Professors Long Chen, David Lesmond, and Jason Wei, who analyzed 4,000 corporate bonds spanning both investment-grade and speculative categories over a nine-year period between 1995 and 2003.29 They used bond rating as a proxy for default risk (a related study was done by Professor Francis Longstaff, but he used a much smaller sample of only 68 issuers and used credit default swaps as proxy for default risk30). The most interesting finding of Chen, Lesmond,


and Wei’s study is the consistent significance of market liquidity in explaining bond yield spreads. This holds true across both investment-grade and speculative-grade bonds. An improvement in market liquidity coincided with a reduction in yield spreads for both investment-grade and speculative-grade bonds. These empirical results also highlight a depen-dence between liquidity and credit. Lower-rated bonds are more illiquid. In particular, for investment-grade bonds, liquidity decreases when mov-ing from AAA bonds to BBB bonds. For speculative-grade bonds, the trend of decreasing liquidity with increasing default risk was less apparent. The empirical results show two general patterns: corporate bonds with higher credit ratings tend to be more liquid, and corporate bonds are less liquid during economic downturns. This holds true particularly for riskier bonds.

Summary statistics classified by bond maturity show an increase in costs as we move from short- to long-maturity bonds.31 These observations are consistent with the more general investment horizon argument, which we discussed in chapter 4. For finite-maturity securities such as bonds, inves-tors can receive the redemption value at maturity without incurring liquida-tion costs. If investors knew exactly when they would liquidate investments, in other words, if their holding periods were certain, they could match the maturity of their corporate bond investment with their investment hori-zon. Empirical evidence presented by Amihud shows that there is no per-fect match between investors’ investment horizon and securities’ maturity. Instead, investors expect that, with a positive probability, they need to sell securities before maturity, at which time they will incur additional transac-tion costs. It follows that a finite maturity security is likely to incur transac-tion costs through its life, and that these costs can be incurred repeatedly because each buyer has these expectations anew, giving rise to a liquidity difference with maturity.32 In general, an investor with an uncertain hori-zon faces a trade-off between buying short-maturity securities, which may expose him to reinvestment risk, and long-maturity securities, which may result in the payment of transaction costs when selling before maturity.

Liquidity risk and aggregate market conditions

The time variation of corporate bond liquidity exhibits a substantial level of commonality, indicating that it is a rather important systematic risk


component, establishing that liquidity risk should indeed factor in asset prices and asset pricing models.

Bond market liquidity commoves in an important way with aggre-gate market conditions. Bao, Pan, and Wang performed a regression analysis of monthly changes in bond market liquidity, measured using the modified Roll measure discussed above, and changes in the Chicago Board Options Exchange Volatility Index (CBOE VIX). The regression coefficients were statistically significant, with an r-squared of around 67 percent for the period 2003 through 2008. The regression coefficients were also statistically significant if the sample excludes the 2008 cri-sis period (the r-squared of around 33 percent).33 This is an intriguing result showing a close relationship between the CBOE VIX and bond market liquidity. The VIX captures the pricing of the Standard & Poor’s (S&P) 500 index options, often referred to as the “fear gauge” of the market. This study indicates a nontrivial interaction between shocks to bond market liquidity and shocks to the appetite for risk.

Quantifying the liquidity premium in defaultable bonds

Assume you invested in a zero-coupon bond that promises to pay $1 at a maturity date T. If the issuer of the bond is default free, and market liquidity is negligible, the price of the zero-coupon bond is simply the discounted present value of $1, which can be calculated using a risk-free interest rate to account for the time value of money. Further assume that the issuer of the bond has some credit risk and that there is a possibil-ity that the issuer may default prior to the maturity date T. In addition to the effect of the time value of money and the uncertainty of future interest rates, both the magnitude and the timing of the cash flow paid to the investor may be uncertain. For ease of exposition, we can view a defaultable zero-coupon bond as a portfolio of two securities: a security that pays $1 at date T contingent on the issuer surviving to the matu-rity date, and a security that pays an amount equal to the recovery in the event of a default before the maturity date.34 The first order of busi-ness would therefore be to incorporate the probability of an issuer default in the bond-pricing model. However, as discussed in chapter 4, market


liquidity induces a liquidity premium (or discount) that causes the price of a security more generally to deviate from its fundamental value.

Developing an asset pricing model for defaultable bonds that allows markets to be illiquid is challenging because it calls for the simultaneous treatment of credit risk and market liquidity. We discuss two frameworks for pricing of defaultable securities that differ in their treatment of credit risk. The pricing of market liquidity is incorporated into both frame-works in the form of a discount rate or an effective discount rate adjusted to account for market liquidity.

The pricing of credit risk has evolved around two formulations of firm default. The structural framework starts with assumptions of the firm’s capital structure, in particular the firm’s outstanding debt and equity. The model assumes that default is based directly on the issuer’s ability or willingness to pay its liabilities, which in turn depends on the value of its assets. The structural framework postulates a process of the evolution of the firm’s asset value over time. The structural model is intuitive—if an analyst wants to understand the impact on credit quality of increased borrowing, share repurchases, or the acquisition of another firm, the structural model naturally lends itself to understanding the implications of these.

The reduced-form framework assumes that a firm’s default is unpre-dictable and that information about the timing of default and expected recovery is not readily available. The reduced-form framework postulates an exogenously specified process for the evolution of default probabili-ties. The specification of the reduced-form framework is mathematically elegant but rather abstract—the default probability is defined as a sto-chastic process—which does not have the intuitive relationship to the firm’s capital structure that we found in the structural framework. Given the nonintuitive nomenclature of the structural and reduced-form credit models, it is helpful to distinguish between these in terms of the infor-mation set that each relies upon: the reduced-form framework utilizes information from the markets, such as traded security prices, while the structural model utilizes information generally available to a firm’s man-agement or regulators, such as detailed information about assets and liabilities.35


Reduced-form model with liquidity

A reduced-form credit models assumes that default is unexpected and not induced by market observables or economic fundamentals. The reduced-form model we discuss in this section incorporates market liquidity, and it further assumes that credit and liquidity risks are independent.36 The assumption of independence allows us to separately measure the credit and liquidity components of the bond price. We can employ this reduced-form framework to quantify market liquidity or other nondefault-related components in corporate bond prices.

Modeling default risk

To capture the uncertainty in the timing of the issuer default, we assume that default is stochastic and we further assume that it can be modeled as a risk-neutral intensity process.37 The value of the zero recovery default-able bond in terms of the intensity process λt and the interest rate rt is shown in equation (5.5) (we assume that expectations are risk neutral):

P t T E et

r duT

u u

, .( ) =∫

− +( )0

λ

(5.5)

A well-known model that would accommodate a nonnegative default intensity is the square-root or Cox-Ingersoll-Ross (CIR) model under which the default intensity has a noncentral chi-square distribution.

The risk-neutral dynamics of the intensity process λt are defined as a Cox-Ingersoll-Ross process:

d dt dZt t tλ α βλ σ λ λ= −( ) + , (5.6)

where α, β, and σ are positive constants, and Z is a standard Brownian motion. These dynamics allow for both the mean reversion and condi-tional heteroskedasticity.

Modeling liquidity

Following the model proposed by Professors Francis Longstaff, Sanjay Mithal, and Eric Neis,38 we assume that the risk-neutral dynamics of


the liquidity process γt can be modeled as a simple Ornstein-Uhlenbeck stochastic process:

d dZγ η γ= , (5.7)

where η is a positive constant and Zγ is a standard Brownian motion. These dynamics allow the liquidity process to take on both positive and negative values.

Professor Robert Jarrow developed an argument to justify the selec-tion of the Ornstein-Uhlenbeck stochastic process to model market liquidity.39 Jarrow’s argument is consistent with no-arbitrage opportuni-ties in an incomplete bond market, such that, one cannot synthetically construct a particular defaultable zero-coupon bond. Further assume that the market is illiquid, so one cannot readily buy or sell the bond. The price of the bond will accordingly be different than the fundamental value due to market liquidity effects.

In particular, assume that the following no-arbitrage relationships hold between the fundamental value, V(t, T), of the bond and the trade price, p(t, T) of the bond.

The first case represents an illiquid market in which one cannot readily buy the bond.

V t T p t T, ,( ) ( )≤ , assuming that there is a reduced supply of bonds.

The second care represents an illiquid market in which one cannot readily sell the bond.

V t T p t T, ,( ) ( )≥ , assuming that supply exceeds demand of bonds.

A simple Ornstein-Uhlenbeck stochastic process γt succinctly captures these relationships. More formally we have

V t T e p t Tt, , .( ) = ( )−γ

In a shortage, one cannot readily buy the bond, or put differently, γt ≤ 0, and the function is positive. This case is analogous to positive convenience yields associated with the storage of production com-modities, such as oil. It is advantageous to hold a bond if the demand is greater than the supply of the bond.


When a glut exists and one cannot readily sell the bond, γt ≥ 0 and the function −γt is negative. This case is analogous to negative convenience yields associated with storage of spoilable commodities such as wheat.

Asset pricing model

The asset pricing model is simplified by assuming that each of the sto-chastic processes for λt and γt evolve independently of each other.40 The value of the zero recovery defaultable bond with liquidity risk is

P t T E et

r duT

u u u

, .( ) =∫

− + +( )0

λ γ

This equation makes it clear that under this formulation, liquidity is accounted for as an adjustment to the discount rate.

This model provides tractable formulas that can easily be expanded to price a coupon bond. Let c denote the coupon rate for a corporate bond, assumed for simplicity to pay coupons continuously. Further assume that the bondholder recovers a fraction δ of the par value of the bond in the event of default of the issuer.41

The price of the coupon bond with maturity T is expressed as

P t T E c e dt E et

T r du

t

t

u u u

, ,δλ γ

( ) =∫

+∫− + +( )

0

0

−− + +( )∫

0

T

u u ur duλ γ

+∫

∫− + +( )

E e dtt

T

t

r dut

u u u

δ λλ γ

0

0 . (5.8)

The first term in the expression in equation (5.8) is the present value of the coupon payments, the second term is the present value of the principal pay-ment, and the third term is the present value of the recovery in the event of a default. Notice that each of these component cash flows is discounted at the adjusted discount rate (rt + λt + γt) that incorporates the liquidity and default risks.

The assumption of independence between the liquidity Ornstein-Uhlenbeck stochastic process and the square-root dynamics of the


intensity process γt, allow us to simplify the pricing formula in equation (5.8).42 The bond-pricing formula becomes,

B t T c A C e D e dt A C e D eT

t tB

tt

T TB

TTt T, ,δ λ λ λ λ( ) = +∫ −( ) − −( ) −

0

+ +( )∫ −( ) −δ λλ λ

0

T

tB

t t ttC e D G H e dtt ,

(5.9)

where λ and γ denote the current (the time-zero) values of the default intensity and liquidity processes, respectively, and where

A tet t=

+( )

−−

exp ( ) ,α β φ

σκ

κ φ

ασ

2

211

2

Bet t=

−+

−( )β φσ

φσ κ φ2 2

21

,

C tt =

exp ,η2 3

6

G e tet

tt= −( ) +

−−

+α α βσ

κκφ

φφφ

ασ1 1

12

2 12exp( )

( ) ,

H tet t=

+( )+

−−

+exp ( ) ,

α β φ φσσ

κκ φ

ασ

2

2

2 211

2

φ σ β= +2 2 2, and

κ β φ β φ= +( ) −/( ).

Comments on Model Implementation

In order to apply the pricing model, we need to augment the frame-work with information on the credit risk of the defaultable security. The assumption of independence between the default and liquidity processes allows us to solve the parameters of the default process directly using information from credit default swaps.43

The remaining parameters can be solved by applying a least-square optimization procedure to the traded bond price. A similar methodology


can be followed to estimate the parameters using a specification in which we fit the yield of traded bonds, rather than their prices.

In the event that a term structure of credit default swap spreads is read-ily available for the particular bond issuer, it is also feasible to solve for the probability of default directly from the term structure of credit default swap spreads.44 This methodology is more direct, and it does not need any additional assumptions regarding the distributional properties of the default process. The latter approach also has the advantage that it pre-serves the information of the credit default swap term structure, which is particularly useful in applications such as solvency analysis, risk manage-ment, and corporate valuation.

The reduced-form model we presented is useful because it offers a tractable pricing formula that can be solved relatively easily using infor-mation from traded bonds and credit default swap spreads. A limitation of this approach is that it ignores the intrinsic interaction between credit risk and liquidity, which may lead to an underestimation of the liquidity premium.45 The pricing framework we discuss in the section titled “A structural credit model with bond market illiquidity” incorporates the dependence between credit and liquidity into the pricing formula.

A structural credit model with bond market illiquidity

Structural credit models are based on insights from Robert Merton for-malized as the seminal Merton model. The Merton model assumes that the firm’s assets, financed with a combination of equity and debt, evolve randomly over time and that claims on the firm’s assets can be valued using option-pricing methods.46 The model further assumes the firm issued a single zero-coupon bond and that the firm might default at maturity of the bond if the total assets are insufficient to meet the obligations due at the bond’s maturity.

The model assumes that the market value of the firms’ assets varies over time, and that this variation is uncertain (random)—the model does not specify the reason for such changes, which makes it applicable to a broad range of financial and nonfinancial firms. We capture these assumptions mathematically by assuming that the market value of the firms’ assets follows a log-normal diffusion.47 The classical Merton model further


assumes that markets are frictionless, in other words, trading it is not plagued by transaction costs and other issues related to market liquidity. The firm’s equity is priced with the Black-Scholes formula as though it is a call option on the total assets value of the firm, struck at the face value of debt.48 The value of debt is then simply obtained by subtracting the equity option price from the initial asset value.

Based on the idea of valuing claims on a firm’s assets using option pric-ing, a class of models developed that introduced more realistic assump-tions. Corporate bond investors require compensation for the uncertainty in the timing of default and the uncertainty in the size of losses due to default (loss is largely determined by the level of the default boundary, defined as the level of assets when default occurs). In “first-passage” models, spearheaded by the famous economists Fisher Black and John Cox, default occurs when assets drop to a level sufficiently below the default boundary, whether or not this occurs at the maturity of the debt.49 Extensions by Professors Hayne Leland and Klaus Toft introduced taxes and bankruptcy costs, which allowed a formal characterization of the optimal capital structure of a firm with endogenous bankruptcy.50 The Leland and Toft formulation allowed the firm to choose the maturity and the amount of debt it issues. This introduced the idea that when a bond matures, the firm usually issues a new bond to maintain their capital. The market price of the new issuance can be lower or higher than that of the maturing bond. The firm’s equity holders absorb the gain or loss due to debt rollover, so the equity value of the firm is determined not only by the value of the unlevered firm but also by the expected future rollover effects. When the equity value drops to zero at the default boundary, the bondholders recover a discounted asset value.

Professors Zhiguo He and Wei Xiong further extended the framework by introducing an illiquid secondary bond market.51 They assume that a bond market investor is exposed to an exogenous market liquidity shock, upon which the holder of the bond is forced to sell his holdings. The sale occurs at a discount to the fundamental value of the bond in a frictionless market. He and Xiong introduce the interaction between market liquidity and credit risk by assuming that the firm refinances or rolls over maturing debt. In the event that the refinancing occurs in an illiquid debt market,


the firm suffers a loss, which causes the firm to default at a higher fun-damental threshold, even without constraints to the firm’s ability to raise more equity. The extension by He and Xiong provides fertile ground for studying the interaction between credit risk and bond market liquidity. Our discussion closely follows that of He and Xiong—we refer to their model as the structural HX model.

Illiquid bond markets and credit risk

Assume that a bond market investor is exposed to a market liquidity shock, which arrives according to a Poisson occurrence with intensity ξ.52 Upon the arrival of the liquidity shock, the bond investor has to exit the investment by selling his bond holding in the secondary market at a cost κ. The transaction cost could represent the bid-ask spread charged by dealers discussed in chapter 4. The bond’s value will be lower than the value in a frictionless market.53

Further assume that the firm replaces maturing debt to maintain its capital structure. A key feature of this model is that maturing debtholders are paid in full, while the equity holders of the firm bear the rollover gains and losses. Assume that gains are paid out to equity holders, losses are paid by issuing additional equity, which dilutes existing shares. Consider a simple example to illustrate these concepts. Suppose a firm has one billion shares of equity outstanding, and each share is initially valued at $10. The firm has $10 billion debt maturing. Assume that the bond market experi-ences an unexpected decrease in liquidity at the maturity of the bonds so that the same amount of bonds is worth only $8 billion due to a nega-tive liquidity premium. To cover the $2 billion shortfall, the firm issues more equity. The proceeds from the share offering are used to fully pay off the maturing debtholders, but the new shares dilute the existing shares and thus reduce the market value of each share. If the firm only needs to roll over its debt once, then it is easy to compute that the firm needs to issue 1/8 billion shares, and each share is valued at $8. The $2 price drop reflects the rollover loss borne by each share. If the firm needs to roll over more debt in the future and the debt market liquidity problem persists,


the share price should be even lower due to the anticipation of future rollover losses.

The example illustrates that the equity value is jointly determined by the firm’s fundamental value and expected future rollover gains/losses. Equity holders are willing to buy more shares and bail out maturing debtholders as long as the equity value is still positive, or put differ-ently, the option value of keeping the firm alive justifies the expected rollover losses. We ignore any additional frictions in the equity markets such as transaction costs and asymmetric information and assume that the firm is not limited in the amount of equity it can issue. The value of the equity is, however, affected by the firm’s debt rollover losses.

Asset pricing model

Assume that the assets of a generic firm, financial or nonfinancial, change over time and that such changes are random. We do not explic-itly define the origin of such changes. The evolution of assets over time At is

dAA

r dt dZt

tt= −( ) +δ σ ,

(5.10)

where r is the constant risk-free rate (assumed to be constant and exog-enous), δ is the firm’s constant cash payout rate, σ is the constant asset volatility, and Z tt : 0 ≤ ≤ ∞{ } is a standard Brownian motion, represent-ing random shocks to the firm’s value. The firm’s assets are financing with a combination of finite maturity debt and equity.

At each point in time, the firm has a portfolio of m bonds outstanding. Each bond has a finite maturity of τ, an annual coupon payment c, and a principal value of p. The firm commits to maintaining a stationary debt structure, so a maturing bond will be replaced by issuing a bond with the same principal value, maturity, and coupon. Expirations of the bonds are uniformly distributed over time. During each time interval (t, t + dt), a fraction

1mdt of the bonds mature and needs to be rolled over. Due to the

liquidity risk of the bond market, the market price of the new bond issued to replace the maturing bonds can be higher or lower than the required


principal on the maturing debt. We assume that equity holders bear the gains or losses from the debt rollover.

An investor in the firm’s bonds requires compensation for several risks involved in holding the bond. The investor’s expected return from holding the bond is determined by the cash flow from the coupon payment, which is offset by the potential loss caused by the occurrence of a market liquidity shock, the time decay due to a change in time-to-maturity τ and the potential default of the issuer firm due to changes in the value of the firm’s assets At.

The required (dollar) return from holding the bond can be expressed as a partial differential equation:

rd A c A d A A d AA

A

t tt

tt

t

( , ) ( , ) ( , )τ ξ τ τ δ τ= − − ∂∂

− ∂∂

∂

kd( , ) + (r )

+

τ

σ12

2 222

2

d AA

t( , ) .τ∂ (5.11)

The terms on the right-hand side represent the contributors to the expected return from holding the bond: the coupon payment c, the loss due of a market liquidity shock, the time decay or loss due to a change in the time-to-maturity τ, and the effects due to changes in the value of the firm’s assets At, shown in the last two terms. The liquid-ity shock hits with probability ξdt. Upon its arrival, the bondholder is forced to sell their holding, and they suffer a loss due to transaction cost of kd(At, τ).

To obtain additional insight into the effect of market liquidity, we rewrite equation (5.11) as

r k d A cd A

r Ad A

A

Ad A

tt

tt

tt

+[ ] ( ) = −∂ ( )

∂+ −( ) ∂ ( )

∂

+∂

ξ ττ

τδ

τ

σ

,, ,

,12

2 22 ττ( )

∂A2 . (5.12)

The formulation in equation (5.12) shows that market liquidity is incor-porated as an effective discount rate (r + ξk), which will effect the valua-tion of the bond.

To price the bond, we can utilize the following two conditions. The firm defaults when its equity value drops to zero at an endogenously


determined threshold AB. At default, the bondholders are entitled to the liquidation value αAB, which is usually below the face value of the debt.

More formally, we can express these two conditions in terms of so-called boundary conditions:

(a) At the default threshold AB, bondholders share the firm’s liquidation value proportionally, thus d V V

Vmt B

B( , ; )τ = α , for a recovery rate of α.

(b) At maturity of the bond, the holder gets the principal value p if the firm survives, d V V pt B( , ; )τ = , for all Vt > VB.

To determine the bond price, we apply the boundary conditions specified in (a) and (b) to solve the partial differential equation in equation (5.12). The value of the bond according to the HX structural model is defined as:54

d A A cr k

e p cr k

F

Am

cr k

t Br k

B

, ,τξ ξ

τ

αξ

ξ τ( ) =+

+ −+

− ( )( )

+ −+

− +( ) 1

( )G τ .

(5.13)

where F(τ) is the cumulative distribution function of the first passage to bankruptcy and 1− ( )( )F τ represents the probability of survival. The bond-pricing equation contains the usual elements, the time-to-maturity τ, the discounted coupon payment, principal payment in the event that the firm survives until the bond matures, and the recovery payment in the event of the firm’s default. The novel feature of this model is that each of these components is discounted at an effective discount rate (r + ξk) that incorporates a liquidity premium ξk.

The bond credit spread, defined as the difference between the yield-to-maturity, assuming the firm survives until bond maturity, and the risk-free rate, contains both a liquidity premium and a default premium because the bond price in equation (5.13) includes the effects of the firm’s potential bankruptcy and the effects of market liquidity. The relationship between the yield-to-maturity y and the bond price is

d A cy e pet

y y, .τ τ τ( ) = −( )+− −1


The HX model renders two channels of interaction between mar-ket liquidity and bond pricing. The first channel through which market liquidity effects transpire is directly as an increase in the liquidity pre-mium kξ. If either the cost of trading increases or the intensity of the liquidity shock to the market (i.e. ξ) or both increase, then bond prices decrease. We discussed many factors earlier that can increase the cost of trading. The intensity of a liquidity shock can increase, for example, if funding is constrained due to increases in margin risk or if funding is less secure due to large-scale investor redemptions.

The second channel is more indirect and market liquidity effects tran-spire when debt needs to be refinanced (so called rollover risk). In the event that the liquidity premium increases, the price of newly issued bonds are suppressed, causing an increase in equity holders’ rollover losses. As a result, equity holders become more reluctant to keep the firm alive even though the falling bond price is caused by deterioration in market liquidity rather than the firm’s fundamental value. The default threshold increases, which in turn leads to a greater default premium in the credit spread.

Numerical example of a structural model with market liquidity

To illustrate the effect that a change in the intensity of the liquidity shock or the transaction cost has on the value of the bond, consider the follow-ing numerical example adapted from He and Xiong. Consider the effects of a deterioration in market liquidity on credit risk: we consider two firms with different credit ratings and compare the change in the bond yield spread of these firms relative to changes in bond market liquidity. We select firms with two representative credit ratings: an A-rated investment-grade firm and a BB-rated speculative-grade firm. For each credit rating, assume each firm has outstanding debt with maturities of one, three, six, and ten years. Using the formulations of the bond price in equation (5.13), we also specify the following values for the respective parameters, namely the asset volatility and the bond-trading cost. Assume that the investment grade firm has an asset volatility of 21 percent and a bond-trading cost of 0.5 percent (50 basis points) while the speculative grade firm has an asset volatility of 23 percent and a bond trading cost of 1 percent (100 basis points). For each A-rated firm, we calibrate its leverage so that the firm


issues new bonds at par and these bonds have a credit spread of 100 basis points at issuance. For each BB-rated firm, we calibrate its leverage so that its newly issued par bonds have a credit spread of 330 basis points.

We report the yield spread for a baseline of liquidity shock of one, but also consider liquidity shocks of two and four. An increase in the liquidity shock from one to four is representative of a severe crisis shock, similar to what was experienced during the 2008 financial crisis. If the liquidity shock increases from one to two, the liquidity premium doubles from 100 basis points to 200 basis points for the BB-rated firm and from 50 to 100 basis points for the A-rated firm. If the liquidity shock increases from one to four, the liquidity premium quadruples.

The results of this numerical example are shown in Table 5.1 for the speculative-grade firm and in Table 5.2 for the investment-grade firm.

Table 5.1 Change in the credit spread of a speculative-grade firm in response to different size shocks in market liquidity

Liquidity Shock = 2 Liquidity Shock = 4

Bond Maturity (years)

Par Bond Yield Spread

(bps)

Increase in Yield Spread

(bps)

Default Component (fraction of

spread change)


(bps)

Default Component(fraction of

spread change)

1 330 169.6 41.0% 523.0 42.6%

3 330 144.6 30.8% 422.1 28.9%

6 330 128.9 22.4% 369.8 18.9%

10 330 120.3 16.9% 341.9 12.3%

Table 5.2 Change in the credit spread of an investment-grade firm in response to different size shocks in market liquidity

Liquidity Shock = 2 Liquidity Shock = 4

Bond Maturity (years)

Par Bond Yield

Spread(bps)


(bps)


spread change)


(bps)


spread change)

1 100 61.7 18.8% 190.7 21.3%

3 100 57.2 12.6% 174.3 13.9%

6 100 56.4 11.3% 166.9 10.1%

10 100 53.7 6.9% 159.7 6.1%


The numerical example shows that the change in the credit spreads of BB-rated firms are greater for a particular size shock to market liquid-ity than A-rated firms. Note that the increase in the credit spread for both liquidity shocks is greater for the speculative grade firm than for the investment grade firm. Furthermore, if we compare the results of the speculative and the investment grade firms for a given debt maturity, the default risk contributes a greater fraction of the credit spread increase for the BB-rated firm. This result makes intuitive sense—the speculative BB-rated firm is closer to its default boundary and is therefore more vul-nerable to any increase in default boundary caused by a shock to market liquidity. This numerical example also sheds some light on the flight-to-quality phenomenon in the bond market whereby investor demand for high-quality bonds increases relative to lower-quality bonds after major liquidity disruptions in financial markets. The yield spreads (prices) of low-quality bonds increase (decrease) more than similar maturity high-quality bonds.

Implications for asset pricing and risk management

The dual nature of default risk and market liquidity risk for corporate bonds in particular and for defaultable securities more generally has implications for investors, issuers, risk managers, and regulators. Investors should incorporate not only the cost of investments but also the cost of liquidating their bond holdings before maturity into their portfolio deci-sions. The frameworks we discussed will allow risk managers to incor-porate the default premium and a liquidity premium in corporate bond pricing, thereby improving the accuracy and reliability of risk measures.

Firms maintain debt financing that needs to be refinanced as bonds mature. The rollover mechanism illustrates that worsening second-ary market liquidity can affect a firm’s solvency due to earlier default. An interesting application of these pricing models is that it allows the quantifying of the effects of liquidity-provision policies on the corporate bond market. A market-wide liquidity provision not only reduces inves-tors’ required compensation for bearing liquidity risk but also alleviates some default risk faced by bond investors. A better functioning financial


market helps mitigate a firm’s rollover risk and thus relaxes its default risk.55 Market liquidity also plays a central role in the size of credit losses for debt instruments. A better understanding of the interactions between bond credit risk and market liquidity is useful for regulators as such rela-tions allow them to adopt regulatory polices to better promote competi-tion and efficiency.

6 Financial Regulation and Liquidity Risk Management

Introduction

The celebrated author Peter Bernstein explained in his book Capital Ideas: The Improbable Origins of Modern Wall Street, “Without the stock market, the market for corporate ownership would be like the market for houses. Agents have to advertise or use some cumbersome method of finding the other side of the deal. Real estate agents earn commissions of 6% or more, while the commission on a typical stock transaction is less than 1%. After the house has been sold, only the principles and their close friends know what the price was.”1 The increased marketability of financial instruments and the transferability of risks have been one of the major features of the modernization of the financial system and our increased reliance on a liquid market for smooth operation of the finan-cial system.

One of the major themes we developed in previous chapters is that lower market liquidity leads to lower prices of securities and therefore higher required return by investors. A more liquid market lowers the cost of capital of firms that rely on the issuance of securities such as debt and equity to raise capital. A lower cost of capital increases investment and affects corporate performance. The proper design of market structure and other arrangements of trading, discussed in chapter 3, can increase the liquidity of traded assets and reduce investors’ required returns. These improvements also affect the value of firms, as shown in a study by profes-sors Vivian Fang, Thomas Noe, and Sheri Tice, who analyzed the effects a decrease in the tick size for US securities had on the value of firms with securities affected by this change. Fang, Noe, and Tice presented


Financial Regulation


firm-level evidence that liquidity is correlated to company value. Firms with more liquid stocks, identified by the increased market liquidity due to the smaller tick size, have higher market-to-book value ratios.2 While these types of structural changes improve market liquidity, individual firms can also take steps to fine-tune the liquidity of their securities. We discuss examples of corporate policies that firms can adopt to increase the liquidity of their claims.

Firms also implement risk management and internal control systems, which are aimed to assess and monitor market liquidity risk. According to Arnaud Bervas, “excessively optimistic assessment of market liquidity, i.e. the belief that transactions can be settled at current prices without any notable delays or transaction costs, may be a serious threat to finan-cial stability.”3 It is crucial to address these threats to financial stability by improving the management of market liquidity risk at the firm level.

The dark side of market liquidity is the very illusion that it will always be around. In reality, market liquidity can suddenly disappear from mar-kets, degenerating into a systemic crisis due to contagion across asset markets when the shock in one market also affects other markets. The global market disruptions during the 2008 crisis and subsequent com-prehensive explanations for the mechanisms that led to the crisis, all elu-cidate the importance of sufficiently liquid financial markets. An illiquid market drains wealth, similar to when you are stuck in traffic jam—you consume gas but go nowhere. Economists at the International Monetary Fund (IMF) found that the total amount of government recapitalizations, asset purchases, and guarantees during the period 2007 to 2011 amounted to nearly $5 trillion. This is equivalent to approximately 16 percent of the gross domestic product (GDP) of these economies, or nearly $5,000 per citizen.4

Dislocations appear when market liquidity evaporates from some key markets. For example, during the 2008 crisis, asset prices of many securi-ties, including mortgage-backed securities (MBS) and asset-backed secu-rities (ABS) were low or not available, reflecting not only impairment of the cash flow due from these securities, but also unusually high-risk and liquidity premiums. This episode illustrates the heightened significance of market liquidity for financial stability. We discuss two general mechanisms

Financial Regulation 145

whereby fire sales manifest. These mechanisms provide valuable insight into spread of the systemic liquidity crisis of 2007–2008.

This chapter concludes with an overview of regulatory proposals aimed at avoiding a repeat of the liquidity crisis of 2007 to 2009. The regulatory framework before the recent financial crisis was inadequate. For example, the average risk weight of bank balance sheets, a measure of capital adequacy under Basel I and II, declined from 70 percent to below 40 percent between 1993 and 2008, but the decline did not correspond to the realities of risk in the banking industry.5 Following the severe market disruptions of the crisis, ex-ante regulation of market liquidity risk is at the heart of proposed changes to financial regulations. An objective of the liquidity measures proposed under Basel III and some of the addi-tional measures aimed at identifying systemically important financial institutions are essential steps needed to avoid the pitfalls of systemic market liquidity risks.

Corporate policies to enhance liquidity of issued securities

Higher liquidity is associated with a lower expected return on assets. Companies thus have an incentive to invest resources in increasing the liquidity of their financial claims in order to reduce their cost of capital. Firms can increase the liquidity of their equity claims by going public—a very popular strategy—according to data published by the Securities Industry and Financial Markets Association (SIFMA), initial public offer-ings raised $22.9 billion in 89 deals and an additional $55 billion on 237 secondary market offerings during the second quarter of 2014 alone.6

Going public is costly because of the direct cash outlays involved. For example, major exchanges such as the New York Stock Exchange (NYSE) or the National Association of Securities Dealers Automated Quotations (NASDAQ) require companies to pay application fees, even before going public, and additional listing fees following a successful initial public offering. Stock exchanges also require the listed firms to maintain certain minimum standards of stockholders’ equity, and a number of sharehold-ers among other things. Going public goes hand in hand with a greater separation between ownership and control, and greater transparency of


the company’s business policies. Public listing, however, is no guaran-tee that the shares will be actively traded with narrow bid-ask spreads and informative prices. To achieve this, a company can also act on other fronts.7

We discussed the importance of symmetric information for mar-ket liquidity. In an effort to reduce information asymmetries, firms can accordingly reduce the informational advantage of one group of investors (e.g., the insiders) over another, such as institutional or retail investors, by publishing financial disclosures and occasional announcements and press releases. An example of a regular financial disclosure created by manage-ment of firms in the United States to communicate with investors and analysts is the annual report filed pursuant to the Securities and Exchange Act of 1934, Form 10-K.

Companies would engage in such disclosures voluntarily even if they were not mandatory, to improve the liquidity of their claims—how would investors purchase equity in companies they have not heard of? Companies willingly publish forecasts and other information, and volun-tarily have their publicly traded bonds rated (while not doing the same for their privately placed bonds). Ratings provide investors with more information on the bond, increasing its liquidity and helping to reduce required yield and therefore the cost of capital for the firm. According to a 2014 study published in the respected The Journal of Finance, “the supply of public information [to] show that firms seek to shape their information environments through voluntary disclosure and that such efforts improve their liquidity. The former result confirms the central assumption made in theoretical models of disclosure. The latter result contributes to our understanding of liquidity in financial markets by showing that manag-ers can actively influence the liquidity of their shares and, ultimately firm value.”8

Another measure being used by firms to increase the liquidity of their securities is to utilize the services of underwriters at investment banks. The role of the underwriter is to increase the liquidity of new securities by disclosing information about the issuer and alleviating investors’ appre-hension about trading the new security. Ideally, the underwriter provides a credible “stamp of approval” on the quality of the company and its


future prospects. The underwriter also provides market-making services in the first days after the issuance, thus ensuring the security’s liquid-ity during that period. It is also typical for multiple investment banks to be involved in large issuance. The largest initial public offering (IPO) in history, Alibaba, a Chinese online and mobile commerce company, was jointly underwritten by Credit Suisse, Deutsche Bank, Goldman Sachs, J.P. Morgan, and Citigroup in September, 2014.9

Management of market liquidity risk

Traders and portfolio managers rely on various risk measures tailored to the securities they trade or manage. For example, a fixed income portfolio manager concerned about interest rate risk typically calculates duration, convexity, exposure to volatility, and time decay on a periodic basis to monitor the market risks of their portfolio. While these granular mea-sures are critical for traders, senior management and regulators find a single measure such as value-at-risk (VaR), which summarizes the total risk of the portfolio, more useful. In particular, VaR is an estimate of the maximum loss that may be incurred on a position or portfolio at a given time horizon and a given confidence level. We explain the implications of market liquidity for risk management using VaR.10

Traditional market risk management is ignorant of market liquidity, and care exclusively about portfolio value changes under the implicit assumption of ideal, frictionless markets by using the midprice (or an equivalent of the midprice) to measure portfolio risk. In reality, traders do not realize the midprice; instead, they realize the midprice less the bid-ask spread. Another important facet of market liquidity is that there may be delays in buying or selling securities. Delays due to a lack of counter-parties or lack of sufficient market depth are prevalent particularly when large trades are needed to reposition or hedge a portfolio. Market partici-pants further assume that the liquidation of positions will have no impact on the market and that the bid-ask spread will remain stable irrespective of the size of the position.

Relying on frictionless markets when estimating risk measures there-fore underestimates the true risk in financial markets, because the realized


value upon liquidation can deviate significantly from the market mid-price. There are ad hoc techniques for re-evaluating VaR by artificially increasing the volatility of positions deemed illiquid, or by lengthening the time horizon used for calculating VaR to ensure an orderly liquida-tion of the position. Risk managers can use ten business days instead of the more typical one-day holding period to calculate the market VaR.

Liquidity-adjusted value at risk improves the assessment of market risk by allowing prices to deviation from their fundamental. Put differ-ently, liquidity-adjusted value at risk extends the traditional value at risk calculation to incorporate the cost of liquidity. The concept behind the liquidity-adjusted VaR is simple. Market risk is split into two compo-nents: the return risk, which can be thought of as a pure market risk component (for example, interest rate risk in a fixed income portfolio), and a liquidity risk.

The developers of liquidity-adjusted VaR distinguish between exog-enous liquidity risk, which is determined by the collective behavior of all market participants and is not under the control of any one participant, and endogenous liquidity risk, which is specific to the size of a particular participant’s trading position.11 The relationship between the trade price and the size of a trade is affected by the aggregate size of all concurrent trades in the particular security. If the size of the order is smaller than the quote depth, the cost of immediate execution is half the bid-ask spread. If the size of the order exceeds the quote depth, the cost of immediate execution is greater than half the bid-ask spread. The excess over the half-spread reflects the endogenous liquidity cost. Implementation is inhibited by a lack of reliable data.

We present a liquidity-adjusted VaR framework that incorporates the exogenous liquidity risk. It is easy to implement and uses readily available data.

Liquidity-adjusted value-at-risk

We develop the framework of liquidity-adjusted VaR, assuming a single security and then extend it to a portfolio of securities at the end of the section.12


The basic elements of the traditional VaR are the distribution of returns of the security over a predefined time period, such as one day. There are two important variables to define VaR, the time period, usually one day, and the confidence level, usually 99 percent. The confidence level reflects the degree of certainty of the potential loss.

Asset returns are the log difference of midprices:

r P PP

Pt t tt

t

= − =

−

−

ln ln .11

ln

Taking a one-day horizon over which the change in asset value is consid-ered, and assuming that one-day returns follow a Gaussian normal distri-butions, the VaR at 99 percent confidence level is

VaR P et tt= −

−1 2 33. ,σ

where σ t2 is the volatility of return. Without loss of generality we assumed

a zero mean return.Assume that the average historical transaction cost is representative

of liquidation. The liquidity-adjusted VaR is then defined as regular VaR plus the cost of liquidating positions:

Liquidity Liquidation CostVaR VaRt t= +

Liquidation Cost =+

Ps k

tsσ

2,

where σs is the volatility of the relative spread, k is a scaling factor, and s is the average relative spread defined in terms of the bid and ask market prices.

s = −Offer Price Bid PriceMid Price

.

Standard VaR calculations presuppose a distribution of returns. The distribution may be determined using a parametric, historical simula-tion, or Monte Carlo simulation. The same approaches may be applied to the distribution of bid-ask spreads. However, spread distributions are not normal distribution, and empirical results show that these could


be multimodal. Market liquidity also exhibits significant variations over time characterized by high-liquidity and low-liquidity regimes. The objective of using a historical simulation would therefore be to determine the “worst-case” scenario spread for a given time horizon and confidence threshold. The highest exogenous liquidity cost is thus obtained.

To treat the return risk and liquidity risk jointly, we make the conser-vative assumption that extreme return events and extreme spread events happen concurrently. The correlation between the midprice movements and spreads is not perfect, but it is nevertheless strong enough during extreme market conditions to enable and encourage us to view return risk and exogenous liquidity risk as experiencing extreme movements simultaneously.

Liquidity VaR P e Ps k

t t tst= − +

+

−12

2 33. .σ σ

The factor k represents a “correction factor” that can be used to dis-tinguish mathematically between the return distributions in a normal market and in a stressed market. A factor of k = 1 represents a normal distribution, and k > 1 represents a deviation from normal, which is typi-cal in a stressed market.

Large samples of daily bid-ask spreads on all securities may not be readily available. An alternative measure of market liquidity is the Roll measure, discussed in chapter 4, which seeks to provide an estimate of the implied spread using only observed market prices. Using historical simulation to estimate VaR proceeds by using the historical distribution of returns and the historical distribution of bid-ask spreads or Roll coef-ficients over the same period to estimate the distribution of possible loss-es—including liquidity cost—on a current position.13

In the traditional portfolio VaR, the covariance matrix of asset returns is the key bridge from a single-instrument measurement to portfolio risk. A liquidity-adjusted portfolio VaR could proceed in a similar fashion, but it would require an assumption of multivariate normality of spreads and the estimation of a spread covariance matrix. But spread distribu-tions are not nearly as well behaved as return distributions, and a feasible


alternative would be to calculate a portfolio-level bid-ask spread series by taking a weighted average of the individual bids and asks and then using the instrument-level liquidity-adjusted portfolio VaR to adjust the stan-dard portfolio VaR for exogenous liquidity risk.

Limitation of liquidity-adjusted value-at-risk

Members of the Basel Committee on Banking Supervision pointed out that “the liquidity of traded products can vary substantially over time and in unpredictable ways,” and moreover, “studies suggest that banks’ exposures to market risk and credit risk vary with liquidity conditions in the market.”14 The former statement suggests a stochastic description of the time horizon over which a portfolio can be liquidated, and the latter highlights a dependence between credit risk and market liquidity risk. A framework with a random holding period is discussed in the context of a single portfolio by academic researchers Damiano Brigo and Claudio Nordio.15 We discussed the dependence between credit risk and market liquidity risk in chapter 5.

There are two main limitations of VaR in general and liquidity-adjusted VaR in particular. VaR has limited, if any, predictive power—the popular historical VaR framework is “backward looking,” implicitly assuming that history repeats itself.

The second limitation arises when all industry participants are sub-jected to a similar risk measure. As explained by the empirical research of Lasse Pedersen of New York University,16 “subjecting traders to liquidity-adjusted value at risk gives rise to a multiplier effect,” which causes a feedback between market liquidity and risk management. The multiplier effect discussed by Pedersen can be explained using anec-dotal evidence on the financial market crises that followed the default of Russian bonds in August 1998. Many traders lost money and, simulta-neously, market volatility increased, which caused the VaR measures at many investment banks and other institutions to increase. Traders were forced to liquidate positions to comply with risk limits, which led to falling prices and lower market liquidity that further exacerbated risk-management problems.


Mechanisms of systemic liquidity risk

The lack of market liquidity under stress is generally a symptom of prob-lems that originate elsewhere. Market liquidity ultimately depends on the way in which market participants perceive and respond to risks and returns.

When funding liquidity and market liquidity chase each other deeper and deeper into an abyss, it can prolong and spread a liquidity crisis into a systemic event. A well-researched case in point is the 2007–2008 crisis, which originated in a relatively the small market segment for subprime securities but was catapulted into a global systemic liquidity crisis. The term “fire sale”—adapted from the sale of fire-damaged goods at dis-counted prices and dating back to the nineteenth century—became a pop-ular nomenclature among academics and other observers who developed theories that shed light on the development and spread of the crisis.

A fire sale is basically a sale of securities mandated by the fact that, without the sale, the firm has no other means of obtaining additional funding or paying existing creditors. A forced sale of securities exerts sig-nificant downward pressure on prices away from fundamentals that cause significant losses to sellers.17 When a fire sale leads to a sharp reduction in an asset’s price, similar assets held by other market participants decline in value as well, which might bring them into financial distress and force asset sales. This self-reinforcing process can lead to a downward spiral in asset prices and the net worth of market participants. Fire sales cause direct losses, but the negative externality of fire sales can result in substan-tial second-round spillover losses. According to a study by economists at the Federal Reserve Bank of New York, a moderate one percent shock to assets measured during a relatively stable market conditions during August 2013 produced fire-sale spillover losses of 23 percent of system capital for broker-dealers and 19 percent of system capital for commercial banks. Fire sale spillover losses during the financial crisis were between two to three times larger.18

The other side of market illiquidity in general and fire sales in par-ticular is funding illiquidity—these are essentially two sides of the same coin—a shock to one or the other can cause a negative spiral that leads to systemic liquidity risk.19 Professors Markus Brunnermeier and Lasse


Pedersen looked at the correlation between the increase in margins and the occurrence of liquidity crises. Their results illustrate that the mar-gin increased around each of the liquidity crises of 1987, 1990, 1998, and 2007. Most recently, margins across most asset classes increased signifi-cantly during the summer of 2007 (we discussed this in more detail in chapter 3). Such margin increases are “destabilizing,” in that their high levels force speculators to delever their positions in times of crisis.20

Stress in the funding of market participants can develop due to an inability to raise cash, either through debt or equity financing, or due to large outflows and cash withdrawals from external investors. The liquid-ity crisis of 2007–2008 developed because key market participants such as broker/dealer intermediaries, some hedge funds, and commercial banks experienced one of the two forms of funding illiquidity that led to cor-related fire sales among market participants.21

The first mechanism whereby fire sales occur is when firms are unable to raise debt financing due to leverage constraints. The theory has two components: (a) the amount of debt financing available to an intermedi-ary is proportional to the equity capital of the intermediary times a lever-age multiple, which is set by the lenders and (b) the demand for assets is a function of the total funds (debt plus equity) available to the intermedi-ary. This mechanism is at work at firms such as hedge funds and brokers/dealers that finance security purchases using the repurchase market. The amount of funding, or leverage, available through security repurchases depends on the margin, or haircut, on the security. The haircut is also affected by the market liquidity of the repurchased security. In the event that the security is less liquidity, the margin will increase proportionally, which lowers the amount of leverage available through repurchasing the security. This leads to a decreased demand for the asset as collateral in a repurchase agreement, which decreases the liquidity of the security even further. The increase in margins on structured securities such as ABS and MBS in 2007–2008 decreased the available leverage, which in turn decreased the demand for assets. For some structured securities, no prices were available—an extreme example of an illiquid market. Another example of an escalating liquidity spiral developed when the hedge fund Long Term Capital Management (LTCM) could ultimately not fund its


positions and was taken over by 14 banks in September 1998. The demise of the hedge fund Amaranth is a more recent example in which the amount of risk taking in the fund’s portfolio far exceeded the amount of available capital. In September 2006, Amaranth lost 65 percent of its $9.2 billion assets. The fund’s assets were subsequently transferred to JP Morgan Chase and Citadel Investment Group.22

The second mechanism whereby fire sales can occur is when the equity risk capital of firms is constrained and they are unable to raise additional equity, but face no constraints in raising debt financing. Because the firm has limited equity capital, its management becomes more risk averse. This leads to a disproportionate demand for low-risk assets and a reduced demand for risky assets in states of the world in which the probability of distress of the firm is high. When many firms are close to distress, the demand for risky assets across all firms is low, causing asset prices to fall for those securities.

This mechanism is also at work when losses erode capital levels at banks. Since regulatory capital requirements at commercial banks penal-ize holdings of risky assets in favor of riskless assets, banks respond by shifting their portfolios to favor riskless assets. Banks accordingly require a higher risk premium to purchase risky assets, causing asset prices to fall.

Feasibility of regulating systemic liquidity risk

The two mechanisms we discussed in the prior section highlight sev-eral important vulnerabilities that can lead to a systemic liquidity cri-sis: market liquidity is integrally linked to funding liquidity, and not all securities are equally liquid. Securities have market liquidity profiles— the liquidity of securities also changes with general market conditions.23 Market participants need to be aware of the liquidity profiles of their assets relative to their liabilities. Participants should strive to match the market liquidity profile of assets with the market liquidity profile of their funding.24

Another insight provided by the theory of fire sales is that the liquidity profile of securities depends not only on the type of product and inherent


risks but also on who holds the security. Market participants have differ-ent funding structures as well as different regulatory requirements that drive their corporate strategies and expose their asset holdings to differ-ent risks.

Developing information about incipient market vulnerabilities is very challenging. By the end of 2014, regulators were still grappling with the questions about the types and feasibility of such information. The ex-ante regulation of liquidity risk at individual institutions is an attempt to pre-vent an excessive buildup of exposure to liquidity risk. In the next section, we discuss two of the liquidity proposals being reviewed as part of the Basel III regulatory proposals. One of these regulations, the liquidity cov-erage ratio (LCR), is designed, among other things, to encourage banks to draw down their high-quality liquid assets as opposed to getting rid of illiquid assets at fire sale prices.

Other initiatives involve the development of systemic risk measures that aggregate the risk information across individual institutions. Such measures can be used to monitor the commonality of exposures and their interactions. The complexity and size of the financial system calls for a diversity of legal and institutional constraints and market practices. There is a corresponding diversity of proposals that emphasizes different aspects of systemic risk. As of 2012, there were as many as 30 different propos-als ranging from macroeconomic measures looking at property price and credit-gap indicators to market micro-structure measures of market liquidity.25

Another active area of regulatory development is the designation of some financial institutions as systemically important financial institu-tions (SIFIs). Under the Dodd-Frank Act, a financial firm is designated as a SIFI if it “holds assets that, if liquidated quickly, would cause a fall in asset prices and thereby . . . cause significant losses or funding problems for other firms with similar holdings”.26

Liquidity regulations under Basel III

The central bank governors of the Group of Ten (G10) countries27 estab-lished the Committee on Banking Regulations and Supervisory Practices


at the end of 1974 in response to disruptions in the financial markets, most notably the market turmoil following the breakdown of the Bretton Woods system of managed exchange rates in 1973. The Committee on Banking Regulations and Supervisory Practices was later renamed the Basel Committee on Banking Supervision. It was designed as a forum for regular cooperation between its member countries on banking and supervisory matters. The Committee exists to enhance financial stability by improving supervisory know-how and the quality of banking supervi-sion worldwide.28

The Basel Committee typically focused on regulations to ensure that banks have sufficient capital for the amount of risk they were taking. Before 2008, there were no global liquidity regulations for banks. Nothing prevented banks from relying heavily on short-term markets to finance highly illiquid long-term assets. The events of the 2007–2008 financial crisis forced market participants and the Basel Committee to focus on market liquidity risks and to develop regulations that will prevent the det-rimental effects of an illiquid market.

In December 2010, the Basel Committee published Basel III, which was in many respects a major overhaul of bank regulations. Basel III rep-resents the first time that liquidity risk has been set at the global level.

In particular, Basel III includes a series of rules concerned with increasing the amount of capital that banks have to keep for credit risk, tightening the definition of capital, regulating the financial leverage (or the amount of debt financing relative to equity financing), and regulat-ing counterparty credit risk management. The novel feature of Basel III is that it includes specific guidelines on liquidity risk that need to be met by banks. The two liquidity regulations are the LCR and the Net Stable Funding Ratio (NSFR), which aim to prevent bank insolvency as a result of liquidity pressures. To ensure that banks can implement the new regulations without disruption to their activities, Basel III is being phased in over a long period of time, starting in 2015 and end-ing on December 31, 2019. To provide insight into the thinking behind the liquidity regulations, consider the following example of how fund-ing of liquidity risk can arise at a bank.29 Assume that a bank relies on short-term funding to finance longer-term assets or investments. This


strategy exposes the bank to liquidity risk in the event that the market becomes distressed or market participants perceive the bank to have problems. Suppose that a bank uses 90-day commercial paper to fund its activities. When one 90-day issue of commercial paper matures, the bank refinances it with a new issue. The bank will continue to roll over this short-term funding every 90 days to finance continuing opera-tions. However, in the event that the bank experiences financial diffi-culties—or perceived difficulties—it is liable to become impossible for the bank to roll over its commercial paper because counterparties may not be willing to extent credit to a distressed institution. This type of problem led to the demise of Northern Rock in the United Kingdom and Lehman Brothers in the United States.

The liquidity coverage ratio30

The LCR focuses on a bank’s ability to survive a 30-day period of liquid-ity disruptions. The ratio assumes a complete drawdown of interbank deposits and all other short-term financial instruments of less than one-month maturity. The 30-day period considered in the calculation of this ratio is one of acute stress (as severe as that seen in the 2007–2008 finan-cial crisis) involving a downgrade of the bank’s debt by three notches, a partial loss of deposits, a complete loss of wholesale funding, increased haircuts on secured funding (so the instruments posted as collateral are not valued as highly), and drawdowns on lines of credit. The ratio is defined as

Liquidity Coverage Ratio = High Quality Liquid AssetsNet Cassh Outflows in a 30 Day Period

.

The Basel III regulations require the ratio to be greater than 100 percent so that the bank’s liquid assets are sufficient to survive these pressures.

The development of this ratio acknowledges that securities have dif-ferent market liquidity and credit characteristics. High-quality liquid assets are mostly government bonds and cash, and a defined maximum percentage of mortgages and corporate bonds may be of certain lower quality.


The net stable funding ratio

The NSFR aims to encourage more medium- and long-term funding of the assets and activities of banks, including off-balance sheet exposures as well as capital market activities, and thereby reduce the extent of maturity mismatch at the bank. In theory, this would lower a bank’s probability of liquidity runs and associated default. It is intended to support the institu-tion as a going concern for at least one year if it is subject to firm-specific funding stress.

The NSFR focuses on liquidity management over a period of one year. It is defined as

Net Stable Funding RatioAmount of Stable Funding

Required =

AAmount of Stable Funding.

The numerator is calculated by multiplying each category of funding (capital, wholesale deposits, retail deposits, etc.) by an available stable funding factor (ASF) reflecting their stability.

As shown in Table 6.1, the ASF for wholesale deposits is less than that for retail deposits, which is in turn less than for Tier 1 and Tier 2 capital.

The denominator is calculated from assets and off-balance sheet items requiring funding. Each of these categories is multiplied by a required

Table 6.1 Available stable funding factors for net stable funding ratio

Factor Category

100% Tier 1 and Tier 2 capital. Preferred stock and borrowing with a maturity greater than one year

90% Stable demand deposits and term deposits with remaining maturity less than one year provided by retail or small business customers

80% Less stable demand deposits and term deposits with remaining maturity less than one year provided by retail or small business customers

50% Wholesale demand deposits and term deposits with remaining maturity less than one year provided by nonfinancial corporates, sovereigns, central banks, multilateral developments banks, and public sector entities

0% All other liabilities and equity categories

Source: John. C. Hull, Risk Management and Financial Institutions, third edition, 2012, John Wiley & Sons, Inc.


stable funding factor to reflect the permanence of the funding required. Some of the applicable factors are indicated in Table 6.2.

Basel III requires the NSFR to be greater than 100 percent so that the calculated amount of stable funding is greater than the calculated required amount of stable funding.

Consider the following example of a bank balance sheet, shown in Table 6.3.

Amount of Stable Funding = Retail Depossits + T

× 90% + Wholesale Deposits × 50%iier 2 Capital + Tier 1 Capital × ×100 100% %

Required Amount of Stable Funding = Cassh + Res

× 0% + Goverment Securities × 5%iidential Mortgages + Business Loans

× ×65 85% %

+ Other Assets ×100%

The NSFR is 95 percent, which is less than the required 100 percent under Basel III. The bank therefore does not satisfy the NSFR.

The new rules are tough and have the potential to dramatically change bank balance sheets. However, there is a transition period during which

Table 6.2 Required stable factors for net stable funding ratio

Factor Category

0% Cash; Short-term instruments, securities, loans to financial entities if they have a residual maturity of less than one year

5% Marketable securities with a residual maturity greater than one year if they are claims on sovereign governments or similar bodies with a 0% risk weight

20% Corporate bonds with a rating of AA- or higher and a residual maturity greater than one year; Claims on sovereign governments or similar bodies with a risk weight of 20%

50% Gold; Equity securities; Bonds rated A+ to A-

65% Residential mortgages

85% Loans to retail and small business customers with a remaining maturity less than one year

100% All other assets

Source: John. C. Hull, Risk Management and Financial Institutions, Third Edition, 2012, John Wiley & Sons, Inc.


the effect of the rules will be monitored. It is possible that the rules will be eased somewhat before they are finally implemented.

The core objective of the Basel III rules is to encourage banks to hold higher liquidity buffers and to lower mismatches between assets and liabilities. The debate on whether the new rules will lower the probabil-ity that any individual institution will run into liquidity problems is still ongoing. Basel III rules are also designed to prevent risks at individual banks, and are not intended or designed to mitigate systemic liquidity risks, in which the interactions of financial institutions can result in the simultaneous inability of institutions to access sufficient market liquidity and funding liquidity under stress.31

Unless the liquidity requirements are set at an extremely high level for all institutions, resulting in a prohibitive cost to the real economy, the possibility always exists that a systemic liquidity event will exhaust all available liquidity. In all circumstances, central bank support is warranted to assume that systemic liquidity shortfalls do not morph into large-scale solvency problems and undermine financial intermediation and the real economy. These indicators are not forward looking.

More regulation or less regulation—where do we go from here?

Many new regulations are designed to protect the financial system and the broader economy against the types of market stress following the demise of Lehman Brothers and Bear Stearns in 2008. The bank-ing industry in particular has raised concerns about the costs of new

Table 6.3 Example of a bank balance sheet

Assets Liabilities

Cash 5 Retail Deposits 40

Treasury Bonds 5 Wholesale Deposits 48

Residential Mortgages 30 Tier 2 Capital 4

Small Business Loans 25 Tier 1 Capital 8

Fixed Assets 35

Total Assets 100 Total Liabilities 100


regulation and warned that the new regulatory requirements may con-strain banks’ ability to lend or invest, resulting in a drag on both bank earnings and broader economic growth.32 According to Bill Nelson, a Federal Reserve economist, the rule “will to some extent make credit a bit more costly, but weighed against that is that these regulations will cause financial crises to be less likely and less frequent and less severe.”33

More stringent liquidity regulation can reduce the risk of systemic cri-sis, but there has been a vigorous debate about the negative impact of such regulation due to the implications this has for other aspects of the market. Two areas of concern are the market liquidity for certain over-the-counter (OTC)-traded securities and banks’ response, along other dimensions such as the size and composition of their balance sheets. An interesting case in point is the effects of reduced dealer inventories of corporate bonds. During 2012, the dealer inventories of corporate bonds failed to comply with new capital regulations that were causing increased liquidity premiums in the corporate bond with essentially no change to the fundamental cash flows of bonds.34

Proponents of after-the-fact intervention, such as the central bank’s act-ing as a lender of last resort, argue that regulation is needed to ensure sufficient capital ex-ante, and that market liquidity problems can be dealt with after the fact. This argument is plausible if the lender of last resort provides liquidity assistance to a fundamentally solvent institution. Walter Bagehot succinctly captured the role of a lender of last resort as follows “In times of crisis, the central bank should lend freely (and at a penalty rate) to banks, provided that the banks are solvent and the loans are adequately collateralized.”35 However, as discussed in chapter 5, and as is clear from the experience of the 2008 crisis, credit risk and the probability of default and liquidity risk are not independent. One might argue that the broker-dealer that experiences a liquidity crunch must have some probability of having solvency problems; otherwise, they would have been able to attract short-term funding from the private market.36 These types of arguments call for ex ante liquidity regulation as part of improving financial stability.

The debate on these issues is ongoing and will be for some time until policymakers and market participants strikes the right balance. The


recent crisis revealed the limitations of classical finance theory and inad-equate market regulations. However, we should continue to assess the contributions of academics and regulatory bodies, and not make hasty pronouncements like the author of this of statement from 1876: “this tele-phone has too many shortcomings to be seriously considered as a means of communication. The device is inherently of no value to us.”37

Notes

1 Musings on Liquidity

1. John Kay, 2013, “A Fixation on Liquidity Is Not Healthy for Financial Markets,” Financial Times, September 17, http://www.ft.com/intl/cms /s/0/5e70c3b8-1f83-11e3-aa36-00144feab7de.html#axzz3Xs4Fn1oD.

2. Speech of Ben S. Bernanke, 2008, “Liquidity Provision by the Federal Reserve,” at the Federal Reserve Bank of Atlanta Financial Markets Conference, May 13, Sea Island, GA.

3. The Economist, 2010, “What Caused the Flash Crash? One Big, Bad Trade,” Joint Report of the Securities and Exchange Commission and the Commodity Futures Trading Commission, October 1.

4. Gillian Tett, 2014, “In Parched Bond Markets, Sparks Are Dangerous,” Financial Times, November 27.

5. A drachma is an ancient Greek coin.6. K. Polanyi, C. Arensberg, and H. Pearson, eds. 1971, Trade and

Markets in the Early Empires, Chicago: Regnery.7. The first years of the Roman Empire, between 27 BCE and 284 AD,

are referred to as the principate.8. K. Roberts, 2011, The Origins of Business, Money, and Markets, New

York: Columbia University Press.9. K. Roberts, 2011, The Origins of Business, Money, and Markets, New

York: Columbia University Press10. John Maynard Keynes, 1936, General Theory of Employment, Interest

and Money, New York: Palgrave Macmillan.11. Hernando De Soto, 2000, The Mystery of Capital: Why Capitalism

Triumphs in the West and Fails Everywhere Else, New York: Basic Books.12. John Maynard Keynes, 1936, General Theory of Employment, Interest

and Money, New York: Palgrave Macmillan.13. Incidentally, Keynes offered a similar argument in 1936, some

30 years earlier.

http://www.ft.com/intl/cms/s/0/5e70c3b8-1f83-11e3-aa36-00144feab7de.html#axzz3Xs4Fn1oD

http://www.ft.com/intl/cms/s/0/5e70c3b8-1f83-11e3-aa36-00144feab7de.html#axzz3Xs4Fn1oD

164 Notes

14. John C. Bogle, 2011, “How the Index Fund Was Born,” Wall Street Journal, September 3.

15. The American Law Register identifies the origin of the modern sys-tem of mortgaging real property in early Jewish sacred writings such as the Talmud. The modern form of the “mortgage” is unique and has roots in American independence and the revolutionary government when “the first legitimate commercial bank” was founded in 1781. See http://www.randomhistory.com/1-50/037mortgage.html.

16. K. J. Arrow and G. Debreu, 1954, “Existence of an Equilibrium for a Competitive Economy,” Econometrica, 22, pp. 265–290.

17. D. Diamond, 1984, “Financial Intermediation and Delegated Monitoring,” Review of Economic Studies, Vol. 51, pp. 393–414.

18. R. Levine, 1997, “Financial Development and Economic Growth: Views and Agenda,” Journal of Economic Literature, Vol. 35, pp. 688–726.

19. G. Gorton and G. Pennacchi, 1990, “Financial Intermediaries and Liquidity Creation,” The Journal of Finance, Vol. 45, No. 1, pp. 49–71.

20. H. Demsetz, 1968, “The Cost of Transacting,” Quarterly Journal of Economics, Vol. 82, No. 1, pp. 33–53.

21. R. C. Merton, 1987, “A Simple Model of Capital Market Equilibrium with Incomplete Information,” The Journal of Finance, Vol. 42, No. 3, pp. 483–510.

22. Walter Bagehot, 1971, “The Only Game in Town,” Financial Analysts Journal, Vol. 27, No. 2, pp. 12–14, 22.

23. A. Bervas, 2006, “Market Liquidity and Its Incorporation into Risk Management,” Banc de France, Financial Stability Review, No. 8, pp. 63–79.

24. S. F. Grossman and M. H. Miller, 1988, “Liquidity and Market Structure,” The Journal of Finance, Vol. 43, No. 3, pp. 617–633.

25. F. Black, 1971, “Toward a Fully Automated Exchange, Part I,” Financial Analyst Journal, Vol. 27, No. 4, pp. 28–35.

26. W. Sharpe and G. Alexander, 1990, Investments (4th ed.), Englewood Cliffs, NJ: Prentice Hall.

27. A. Shleifer and R. W. Vishny, 1997, “The Limits to Arbitrage,” The Journal of Finance, Vol. 52, No. 1, pp. 35–55.

http://www.randomhistory.com/1-50/037mortgage.html

Notes 165

28. This section closely follows the discussion in J. H. Cochrane and C. L Culp, 2003, “Equilibrium Asset Pricing and Discount Factors: Overview and Implications for Derivatives Valuation and Risk Management,” in Modern Risk Management: A History, P. Field, ed. London: Risk Books, pp. 57–92.

29. W. Sharpe, 1964, “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk,” The Journal of Finance, Vol. 19, pp. 425–442.

30. F. Black, 1972, “Capital Market Equilibrium with Restricted Borrowing,” Journal of Business, Vol. 45, pp. 444–455.

31. Y. Amihud, A. Hameed, W. Kang, and H. Zhang, 2013, “The Illiquidity Premium: International Evidence,” Working Paper.

2 Financial Crises and Liquidity Traffic Jams

1. John Maynard Keynes, 1936, General Theory of Employment, Interest and Money, San Diego, New York and London: Harcourt.

2. M. K. Brunnermeier, 2009, “Deciphering the Liquidity and Credit Crunch 2007–2008,” Journal of Economic Perspectives, Vol. 23, pp. 77–100; and G. Gorton, 2009, “Information Liquidity and the (Ongoing) Panic of 2007,” American Economic Review: Papers & Proceedings, Vol. 99, No. 2, pp. 567–572.

3. G. Gorton, 2009, “Slapped in the Face by the Invisible Hand: Banking and the Panic of 2007,” Prepared for the Federal Reserve Bank of Atlanta’s 2009 Financial Markets Conference: Financial Innovation and Crisis, May 11–13.

4. J. Coval, J. Jurek, and E. Stafford, 2009, “The Economics of Structured Finance,” Journal of Economic Perspectives Vol. 23, No. 1, pp. 3–25.

5. Marcia Stigum and Anthony Crescenzi, 2007, Stigum’s Money Market (4th ed.), New York: McGraw-Hill.

6. Patrick E. McCabe, September 2012, “The Cross Section of Money Market Mutual Fund Risks and Financial Crisis,” Federal Reserve Board, Divisions of Research & Statistics and Monetary Affairs, Finance and Economics Discussion Series Working Paper No. 2010–51.

166 Notes

7. B. Duygan-Bump, P. M. Parkinson, E. S. Rosengren, G. A. Suarez, and P. S. Willen, 2010, “How Effective Were the Federal Reserve Emergency Liquidity Facilities? Evidence from the Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility,” Working Paper QAU10–3, Federal Reserve Bank of Boston, April 29.

8. US Securities and Exchange Commission, 2010, “Money Market Fund Reform: Final Rule,” available at www.sec.gov/rules/final/2010/ic-29132.pdf, February 23. Release no. IC-29132.

9. US Securities and Exchange Commission, 2010 at 10075, Securities and Exchange Commission, Federal Register, Vol. 75, No. 42, Thursday, March 4, Rules and Regulations.

10. F. A. Longstaff, 2004, “The Flight-to-Liquidity Premium in U.S. Treasury Bond Prices,” Journal of Business, Vol. 77, No. 3, pp. 511–526.

11. O. Vasicek, 1977, “An Equilibrium Characterization of the Term Structure,” Journal of Financial Economics, Vol. 5, pp. 177–188.

12. M. K. Brunnermeier and M. Yogo, February 2009, “A Note on Liquidity Risk Management,” NBER Working Paper No. 14727.

13. Bengt Holmstrom and Jean Tirole, 2011, Inside or Outside Liquidity, Cambridge: MIT Press.

14. P. Pozsar, 2013, Institutional Cash Pools and the Triffin Dilemma of the U.S. Banking System. NYU Stern, Financial Markets, Institutions & Instruments: Topics in Financial Intermediation, New York: New York University Salomon Center and Wiley Periodicals.

15. Bengt Holmstrom and Jean Tirole, 2011, Inside or Outside Liquidity, Cambridge: MIT Press.

16. D. W. Diamond and P. H. Dybvig, 1983, “Bank Runs, Deposit Insurance, and Liquidity,” Journal of Political Economy, Vol. 91, No. 3, pp. 401–419.

17. M. K. Brunnermeier, 2009, “Deciphering the Liquidity and Credit Crunch 2007–2008,” Journal of Economic Perspectives, Vol. 23, pp. 77–100.

18. D. Covitz, N. Liang, and G. A. Suarez, 2013, “The Evolution of a Financial Crisis: Collapse of the Asset-Backed Commercial Paper Market,” The Journal of Finance, Vol. 68, No. 3, pp. 815–848.

www.sec.gov/rules/final/2010/ic-29132.pdf

www.sec.gov/rules/final/2010/ic-29132.pdf

Notes 167

19. G. Gorton and A. Metrick, 2012, “Securitized Banking and the Run on Repo,” Journal of Financial Economics Vol. 104, pp. 425–451.

20. Gorton and Metrick calculated the repo haircut on a basket of securities comprised of an equally weighted portfolio of student loan, credit card, and auto loan ABS, residential mortgage backed securities, commercial mortgage backed securities, subprime mortgages, collateralized loan obligations and collateralized debt obligations and corporate securities.

21. Gorton referred to this dynamic as the first “run on repo.”22. G. Gorton, 2009, “Slapped in the Face by the Invisible Hand: Banking

and the Panic of 2007,” Prepared for the Federal Reserve Bank of Atlanta’s 2009 Financial Markets Conference: Financial Innovation and Crisis, May 11–13.

23. European Central Bank Working Paper Series No 1126, December 2009.

24. D. W. Diamond and R. G. Rajan, 2011, “Fear of Fire Sales, Illiquidity Seeking and Credit Freezes,” Quarterly Journal of Economics, Vol. 126, Issue 2, pp. 557–591.

25. M. Mitchell, L. H. Pedersen and T. Pulvino, 2007, “Slow Moving Capital,” AEA Papers and Proceedings, pp. 215–220.

26. N. Gârleanu and L. H. Pedersen, 2011, “Margin-Based Asset Pricing and Deviations from the Law of One Price,” The Review of Financial Studies, Vol. 24, No. 6, pp. 1980–2022.

27. M. K. Brunnermeier and L. H. Pedersen, 2008, “Market Liquidity and Funding Liquidity,” The Society for Financial Studies, Vol. 22, no. 6, pp. 2202–2238.

28. R. C. Merton, 1987, “A Simple Model of Capital Market Equilibrium with Incomplete Information,” The Journal of Finance, Vol. 43, No. 3, pp. 483–510.

29. V. V. Acharya and L. H. Pedersen, 2005, “Asset Pricing with Liquidity Risk,” Journal of Financial Economics, Vol. 77, No. 2, pp. 373–410.

30. F. A. Longstaff, 2009, “Portfolio Claustrophobia: Asset Pricing in Markets with Illiquid Assets,” American Economic Review, Vol. 99, pp. 1119–1144.

31. B. S. Bernanke, 2007, “The Recent Financial Turmoil and Its Economic and Policy Consequences,” October 15, Economic Club of New York,

168 Notes

New York, http://www.federalreserve.gov/newsevents/speech/ber-nanke20071015a.htm.

32. L. Fisher, 1959, “Determinants of Risk Premiums on Corporate Bonds,” Journal of Political Economy, Vol. 67, No. 3, pp. 217–237.

33. Stéphane Loisel, 2012, “From Liquidity Crisis to Correlation Crisis, and the Need for ‘Quanls’ in ERM,” Risk Management, Issue 25, pp. 16–18.

34. V. Acharya and S. Schaefer, 2006, “Liquidity Risk and Correlation Risk: Implications for Risk Management,” September, Working Paper.

3 Market Structures and Institutional Arrangements of Trading

1. We are interchangeably using the terms “dealer” and “market maker” to mean the entity or market participant fulfilling the role of financial intermediation.

2. A definition of “all available information” is information about histor-ical security prices, public information such as earnings announce-ments, stock splits, etc., and private information relevant for security prices. The reader interested in exploring definitions of “all available information” is referred to work by 2013 Nobel Prize winner Eugene Fama.

3. E. F. Fama, 1970, “Efficient Capital Markets: A Review of Theory and Empirical Work,” The Journal of Finance, Vol. 25, No. 2, pp. 383–417.

4. E. F. Fama, 1970, “Efficient Capital Markets: A Review of Theory and Empirical Work,” The Journal of Finance, Vol. 25, No. 2, pp. 383–417.



7. M. O’Hara, 2001, “Overview: Market Structure Issues in Market Liquidity,” BIS Papers, Market Liquidity: Proceedings of a Workshop held at the Bank of International Settlements, No. 2.

http://www.federalreserve.gov/newsevents/speech/bernanke20071015a.htm

http://www.federalreserve.gov/newsevents/speech/bernanke20071015a.htm

Notes 169

8. D. Easley and M. O’Hara, 1987, “Prices, Trade Size and Information in Securities Markets,” Journal of Financial Economics, Vol. 19, pp. 69–90.

9. A. S. Kyle, 1985, “Continuous Auctions and Insider Trading,” Econometrica, Vol. 53, No. 6, pp. 1315–1335.

10. If trading were solely information-based, uninformed traders would do better to leave the market rather than face a certain loss when trading with informed traders. In this discussion, we assume that uninformed trade occurs due to exogenous demand, such as an imbalance in the timing of consumption and income or from portfolio considerations.

11. A. Akerlof, 1970, “The Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism,” Quarterly Journal of Economics, Vol. 84, No. 3, pp. 488–500.

12. Another popular measure of liquidity is trading volume, but many consider this to be a flawed indicator. High trading volume does not necessarily imply high liquidity, as was made painfully clear during the “flash crash” on May 6, 2010, when the Dow Jones Industrial Average experienced its largest one-day decline of 988.5 points (about 9%) and the second-largest point swing in its history. For a few minutes, $1 trillion in market value vanished. The SEC observed that, “especially in times of significant volatility, high trading volume is not necessar-ily a reliable indicator of market liquidity.” See Findings Regarding the Market Events of May 6, 2010, September 30, 2010, Report of the staffs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues.

13. L. R. Glosten and P. R. Milgrom, 1985, “Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Trades,” Journal of Financial Economics, Vol. 14, pp. 71–100.

14. The asymmetric information model is also an important hypothesis of how information in the order flow becomes impounded in prices, as discussed in the section titled “Market microstructure insights into security price formation.”

15. The “trade-through rule” is absent from regulation MiFID in the European Union.

16. P. Hoffman, March 2013, “Adverse Selection, Market Access and Inter-Market Competition,” European Central Bank, Working Paper, No. 1519.

170 Notes

17. This example closely follows an example discussed in T. Foucault, M. Pagano, and A. Röell, 2013, Market Liquidity: Theory, Evidence, and Policy, New York: Oxford University Press.

18. B. Biais, L. Glosten, and C. Spatt, 2004, “Market Microstructure of Stock Markets, Working Paper.

19. D. Duffie, 2012, Dark Markets: Asset Pricing and Information Transmission in Over-the-Counter Markets, Princeton Lectures in Finance, Princeton, NJ: Princeton University Press.

20. Tabb Report, September 2012, “The New Global Risk Transfer Market: Transformation and the Status Quo,” Tabb Group, V10: 033.

21. Securities and Exchange Commission, 1998, “Regulation of Exchanges and Alternative Trading Systems,” Release No. 34–40760; File No. S7–12–98.

22. The percentage is based on the average daily volume of shares traded during November 2014.

23. Regulation NMS, Exchange Act Release No. 51808, June 2005. Similar legislation in Europe, the Markets in Financial Instruments Directive (“MiFID”), was implemented in November 2007.

24. The four main features of Regulation NMS are the Order Protection Rule, the Access Rule, the Sub-Penny Rule, and the Market Data Rule.

25. BATS Global Markets, http://www.batstrading.com/market_summary/.26. Detailed posttrade information on US corporate bonds is available

through the Trade Reporting and Compliance Engine (TRACE), which provides the price of the transaction within minutes of the trade. For trade sizes less than a stipulated quantity, trade sizes are also reported.

27. Y. Amihud and H. Mendelson, 1980, “Dealership Markets: Market Making with Inventory,” Journal of Financial Economics, Vol. 8, pp. 31–53.

28. D. Duffie, N. Gârleanu and L. H. Pederson, 2005, “Over-the-Counter Markets,” Econometrica, Vol. 73, pp. 1815–1847.

29. D. Duffie, N. Gârleanu and L. H. Pederson, 2005, “Over-the-Counter Markets,” Econometrica, Vol. 73, pp. 1815–1847.

http://www.batstrading.com/market_summary/

Notes 171

30. D. Duffie, N. Gârleanu and L. H. Pederson, 2005, “Over-the-Counter Markets,” Econometrica, Vol. 73, pp. 1815–1847. The rest of this sec-tion closely resembles the discussion in Duffie et al.

31. This dynamic is similar to the trade-off between price and imme-diacy that exists in limit orders versus market orders in order-driven markets, which is discussed in the section titled “Key structural fea-tures of limit order books.

32. M. O’Hara, 2003, Market Microstructure Theory, Malden, MA; Oxford, UK; Victoria, Australia: Blackwell Publishing.

33. B. Biais, L. Glosten, and C. Spatt, 2005, “Market Microstructure: A Survey of Micro-foundations, Empirical Results and Policy Implications, Journal of Financial Markets, Vol. 8, pp. 217–264.

34. T. Foucault, 1999, “Order Flow Composition and Trading Costs in a Dynamic Limit Order Market,” Journal of Financial Markets, Vol. 2, pp. 99–134.

35. CME Group, CME Globex Reference Guide.36. H. Demsetz, 1968, “The Cost of Transacting,” Quarterly Journal of

Economics, Vol. 82, No. 1, pp. 33–53.37. E. Portniaguina, D. Bernhardt, and E. Hughson, 2006, “Hybrid

Markets, Tick Size and Investor Trading Costs,” Journal of Financial Markets Vol. 9, pp. 433–447.

38. L. Glosten, 1994, “Is the Electronic Open Limit Order Book Inevitable?” The Journal of Finance, Vol. 49, pp. 1127–1161.

39. Market participants are considering proposals about alternatives, for example, the implementation of frequent batch auctions proposed by Budish et al. See E. Budish, P. Cramton, and J. Shim, 2015, “The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response,” Working Paper.

40. R. Huang, and H. Stoll, 1997, “The Components of the Bid-Ask Spread: A General Approach,” Review of Financial Studies, Vol. 10, pp. 1035–1064.

41. C. Parlour, and D. Seppi, 2003, “Liquidity-Based Competition for Order Flow,” Review of Financial Studies, Vol. 16, pp. 301–343.


172 Notes

43. H. R. Stoll, August, 2000, “Friction,” The Journal of Finance, Volume 55, No. 4, pp. 1478–1514.

44. E. R. Sirri, 2008, Keynote Speech at the SIFMA 2008 Dark Pools Symposium, New York.

45. E. R. Sirri, 2008, Keynote Speech at the SIFMA 2008 Dark Pools Symposium, New York.

46. H. Zhu, 2013, “Do Dark Pools Harm Price Discovery?” Forthcoming, Review of Financial Studies.

47. The US Securities and Exchange Commission 2010 Concept Release on Equity Market Structure. See Exchange Act Release No. 61358 (January 13, 2010); Exchange Act Release No. 61908 (April 14, 2010).

48. Securities and Exchange Commission, 2010, Regulation NMS, 17 C.F.R. Parts 200, 201, 230, 240, 249 and 270, Release No. 34–51808, File No. S7–10–04, RUN 3235-AJ18 (“Reg NMS”) at 21 and 22.

49. L. Melamed, 2015, “The Day the Shouting Stopped,” The Wall Street Journal, February 11, 2015.

50. D. Easley, M. L. de Prado, and M. O’Hara, eds., 2013, High-Frequency Trading: New Realities for Traders, Markets and Regulators, London: Risk Books.

51. D. Easley, M. L. de Prado, and M. O’Hara, eds., 2013, High-Frequency Trading: New Realities for Traders, Markets and Regulators, London: Risk Books. The rest of this discussion draws heavily from this source.

52. Performance Statistics from NASDAQ, accessed in November 2014, http://www.nasdaqtrader.com/Trader.aspx?id=Latencystats.

53. A. Greenspan, 2000, “Electronic Finance,” Remarks by Chairman Alan Greenspan at the Financial Markets Conference sponsored by the Federal Reserve Bank of Atlanta, Sea Island, Georgia.

54. A bank’s available capital may be adjusted by the amount of assets that cannot readily be employed, such as goodwill, intangible assets, property, equipment, and cash needed for daily operations.

55. M. K. Brunnermeier and L. H. Pedersen, 2009, “Market Liquidity and Funding Liquidity,” Review of Financial Studies, Vol. 22, pp. 2201–2238.

http://www.nasdaqtrader.com/Trader.aspx?id=Latencystats

Notes 173

56. D. Duffie, 2010, “Presidential Address: Asset Price Dynamics with Slow-Moving Capital,” The Journal of Finance, Vol. 65, No. 4, pp. 1237–1267.

57. Default losses are generally determined by the principal value on the bond, less the default recovery rate.

58. Alternatively, if the basis becomes materially positive, arbitrageurs sell credit default swap protection and simultaneously short the cor-porate bond.

59. M. Mitchell and T. Pulvino, 2012, “Arbitrage Crashes and the Speed of Capital,” Journal of Financial Economics, Vol. 104, pp. 469–490.

60. D. Duffie, 2010, “Presidential Address: Asset Price Dynamics with Slow-Moving Capital,” The Journal of Finance, Vol. 65, No. 4, pp. 1237–1267.

61. Financial Stability Report, June 2014, Bank of England, Issue No. 35.62. BlackRock Investment Institute, September, 2012, “Got Liquidity?”,

see, http://www.blackrock.com/investing/literature/whitepaper/got -liquidity-us-version.pdf

63. M. K. Brunnermeier and L. H. Pedersen, 2009, “Market Liquidity and Funding Liquidity,” Review of Financial Studies, Vol. 22, pp. 2201–2238.

64. J. F. Coughenour and M. Saad, 2004, “Common Market Makers and Commonality in Liquidity,” Journal of Financial Economics, Vol. 73, pp. 37–39.

65. J. Dick-Nielsen, P. Feldhütter, and D. Lando, 2012, “Corporate Bond Liquidity before and after the Onset of the Subprime Crisis,” Journal of Financial Economics, Vol. 103, pp. 471–492.

4 Asset Pricing and Market Liquidity

1. F. A. Longstaff, 2004, “The Flight-to-Liquidity Premium in U.S. Treasury Bond Prices,” Journal of Business, Vol. 77, No. 3, pp. 511–526.

2. H. Chen, G. Noronha, and V. Singhal, 2004, “The Price Response to the S&P 500 Index Additions and Deletions: Evidence of Asymmetry and a New Explanation,” The Journal of Finance, Vol. 59, No. 4, pp. 1901–1930.

3. We utilize simplifications of some of the more general models from T. Foucault, M. Pagano, and A. Röell, 2013, Market Liquidity: Theory, Evidence, and Policy, New York: Oxford University Press.

http://www.blackrock.com/investing/literature/whitepaper/got-liquidity-us-version.pdf


174 Notes

4. Y. Amihud and H. Mendelson, 1986, “Asset Pricing and the Bid-Ask Spread,” Journal of Financial Economics, Vol. 17, pp. 223–249.

5. Y. Amihud and H. Mendelson, 1986, “Liquidity and Stock Returns,” Financial Analyst Journal, Vol. 42, pp. 43–48.

6. G. M. Constantinides, 1986, “Capital Market Equilibrium with Transaction Costs,” Journal of Political Economy, Vol. 94, Issue 4, pp. 842–862.

7. R. Roll, “A Simple Implicit Measure of Bid-Ask Spread in an Efficient Market,” 1984, The Journal of Finance, Vol. 39, No. 4, pp. 1127–1139.

8. H. R. Stoll, 2000, “Frictions,” The Journal of Finance, Vol. 55, No. 4, pp. 1479–1514.


10. Y. Amihud and H. Mendelson, 1986, “Asset Pricing and the Bid-Ask Spread,” Journal of Financial Economics, Vol. 17, pp. 223–249.

11. Y. Amihud and H. Mendelson, 1980, “Dealership Markets: Market Making with Inventory,” Journal of Financial Economics, Vol. 8, pp. 31–53.


13. D. Duffie, N. Gârleanu, and L. H. Pederson, 2005, “Over-the-Counter Markets,” Econometrica, Vol. 73, pp. 1815–1847.

14. Diamond P., 1982, “Aggregate Demand Management in Search Equilibrium,” Journal of Political Economy, Vol. 90, No. 5, pp. 881–894.

15. The assumption of repeated trade differentiates the search-and-bar-gaining model from the labor market coconut model.

16. A consol bond is a bond with no maturity date, but it pays interest forever. Another example of a perpetual security would be a stock that pays dividends for an indefinite period.

17. The liquidity premium in this context can be thought of as an illi-quidity discount.

18. D. Duffie, 2010, “Presidential Address: Asset Pricing Dynamics with Slow-Moving Capital,” The Journal of Finance, Vol. 65, No. 4, pp. 1237–1267.

Notes 175

19. A. Shleifer and R. W. Vishny, 1997, “The Limits to Arbitrage,” The Journal of Finance, Vol. 52, No. 1, pp. 35–55.

20. We follow the model in which the arbitrageur is allowed to invest only part of his resources at date 0 and “save” the rest of his resources to intervene at date 1. The more complex model, however, does not offer any additional insights and is not discussed further here. See T. Foucault, M. Pagano, and A. Röell, 2013, Market Liquidity: Theory, Evidence, and Policy, New York: Oxford University Press.

21. This type of arbitrage is denoted as performance-based arbitrage, A. Shleifer and R. W. Vishny, 1997, “The Limits to Arbitrage,” The Journal of Finance, Vol. 52, No.1, pp. 35–55.

22. A. Shleifer and R. W. Vishny, 1992, “Liquidation Values and Debt Capacity: A Market Equilibrium Approach,” The Journal of Finance, Vol. 47, No. 4, pp. 1343–1366.

23. The aggregate supply from arbitrageurs sell orders at date 1 is

ϕ ϕϕ

( )i di =∫ 12

2

0

.

24. V. V. Acharya and L. H. Pedersen, 2005, “Asset Pricing with Liquidity Risk,” Journal of Financial Economics, Vol. 77, pp. 375–410.

25. Under the CAPM, idiosyncratic market risk or company specific risk is not rewarded with higher expected return.

26. T. Foucault, M. Pagano, and A. Roëll, 2013, Market Liquidity: Theory, Evidence, and Policy, New York: Oxford University Press.

27. T. Chordia, R. Roll, and A. Subrahmanyam, 2000, “Commonality in Liquidity,” Journal of Financial Economics, Vol. 56, pp. 3–28.

28. L. Pastor and R. F. Stambaugh, 2003, “Liquidity Risk and Expected Stock Returns,” Journal of Political Economy, Vol. 111, No. 3, pp. 642–685.

29. Richard Bookstaber, former head of risk management at Salomon Bros., 2007, “Wall Street’s money machine breaks down: The sub-prime mortgage crisis keeps getting worse-and claiming more vic-tims,” Fortune, November 26, p. 49.

30. M. K. Brunnermeier and L. H. Pedersen, 2008, “Market Liquidity and Funding Liquidity,” The Society for Financial Studies, pp. 2202–2238.

31. V. V. Acharya and L. H. Pedersen, 2005, “Asset Pricing with Liquidity Risk,” Journal of Financial Economics, Vol. 77, pp. 375–410, and

176 Notes

K. Lee, 2011, “The World Price of Liquidity Risk,” Journal of Financial Economics Vol. 99, pp. 136–161.

32. B. Hagströmer, B. Hansson, and B. Nilsson, 2013, “The Components of the Illiquidity Premium: An Empirical Analysis of US Stocks 1927–2010,” Journal of Banking and Finance, Vol. 37, pp. 4476–4487.

33. Note that Hagströmer, Hansson, and Nilsson implemented a condi-tional version of the LCAPM to allow for time variation in the risk premia. See B. Hagströmer, B. Hansson, and B. Nilsson, 2013, “The Components of the Illiquidity Premium: An Empirical Analysis of US Stocks 1927–2010,” Journal of Banking and Finance, Vol. 37, pp. 4476–4487.

34. R. Goyenko, C. Holden, and C. Trzcinka, 2009, “Do Liquidity Measures Measure Liquidity?” Journal of Financial Economics, Vol. 92, No. 2, pp. 153–181.

35. S. Kim, and K. Lee, 2014, “Pricing of Liquidity Risks: Evidence from Multiple Liquidity Measures,” Journal of Empirical Finance, Vol. 25, pp. 112–133.

36. This section follows the derivation in T. Foucault, M. Pagano, and A. Roëll, 2013, Market Liquidity: Theory, Evidence, and Policy, New York: Oxford University Press.

5 Stories of Liquidity and Credit

1. Lawrence Fisher spearheaded the development and maintenance of the Center for Research in Security Prices (CRSP) databases in the 1960s at the University of Chicago, Booth School of Business, which formed the foundation for decades of empirical research in financial economics.

2. L. Fisher, 1959, “Determinants of Risk Premiums on Corporate Bonds,” Journal of Political Economy, Vol. 67, No. 3., pp. 217–237.

3. While other aspects of bonds such as call and put provisions, tax effects, sinking fund payments, and so forth are also interesting from a pricing and risk management perspective, much work has already been done in these areas, so we will focus on the credit and liquidity aspect of bonds. For a detailed discussion on other aspects F. Farbozzi

Notes 177

and S. V. Mann, 2012, The Handbook of Fixed Income Securities, eighth edition, New York: McGraw-Hill.

4. A. Shleifer and R. W. Vishny, 2011, “Fire Sales in Finance and Macroeconomics,” The Journal of Financial Perspectives, Volume 52, No. 1, pp. 29–48.

5. Robert, C. Merton, 1974, “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,” The Journal of Finance, Vol. 29, Issue 2, pp. 449–470.

6. J-Z. Huang and M. Huang, 2012, “How Much of the Corporate-Treasury Yield Spread Is Due to Credit Risk?” Review of Asset Pricing Studies, Vol. 2, No. 2, pp. 153–202.

7. More information on the Trade Reporting and Compliance Engine (TRACE) can be found on FINRA’s website. See http://www.finra.org/industry/compliance/markettransparency/trace /corporatebonddata/.

8. J. Bao, J. Pan, and J. Wang, 2011, “The Illiquidity of Corporate Bonds,” The Journal of Finance, Vol. 66, No. 3, pp. 911–946.

9. A distinctive feature of most fixed-income markets it that trading takes place in the OTC market, which is dominated by a limited number of market makers. Finding a buyer for a given position can be time consuming and risky because it depends on the willingness of a market maker committed to providing liquidity. The search-and-bargaining model discussed in chapter 4 is particularly relevant for gaining insight into liquidity risk involved in OTC trading. However, the search-and-bargaining model, while useful from a structural market perspective, is limited because it does not take into account corporate default and credit risks.

10. A. Krishnamurthy and A. Vissing-Jorgensen, 2010, “The Aggregate Demand for Treasury Debt,” Journal of Political Economy, Vol. 120, No. 2, pp. 233–267.

11. Amy K. Edwards, Lawrence E. Harris, and Michael S. Piwowar, 2007, “Corporate Bond Market Transaction Costs and Transparency,” The Journal of Finance, Vol. 62, pp. 1421–1451.

12. J. Bao, J. Pan, and J., Wang, 2011, “The Illiquidity of Corporate Bonds,” The Journal of Finance, Vol. 66, No. 3, pp. 911–946.

http://www.finra.org/industry/compliance/markettransparency/trace/corporatebonddata/



178 Notes

13. N. Gârleanu and L. H. Pedersen, 2011, “Margin-Based Asset Pricing and Deviations from the Law of One Price,” Working Paper.

14. A well-functioning interbank market is critical for any OTC trading and bond market trades in particular because of dealers’ reliance on the interbank market as a means of managing inventory, as discussed in chapter 3.

15. G. R. Duffee, 1999, “Estimating the Price of Default Risk,” Review of Financial Studies, Vol. 12, pp. 187–226.

16. N. Gârleanu and L. H. Pedersen, 2011, “Margin-Based Asset Pricing and Deviations from the Law of One Price,” Working Paper.

17. D. Bongaerts, F. de Jong, and J. Driessen, 2011, “Derivative Pricing with Liquidity Risk: Theory and Evidence from the Credit Default Swap Market,” The Journal of Finance, Vol., 66, Issue, 1, pp. 203–240.

18. V. Niederhoffer and M. F. M. Osborne, 1966, “Market Making and Reversal on the Stock Exchange,” Journal of the American Statistical Association, Vol. 61, pp. 897–916.


20. N. Friewald, R. Jankowitsch, and M. G. Subrahmanyam, 2012, “Illiquidity and Credit Deterioration: A Study of Liquidity in the US Corporate Bond Market during Financial Crises,” Journal of Financial Economics, Vol. 105, pp. 18–36.

21. J. Dick-Nielsen, P. Feldhutter, and D. Lando, 2011, “Corporate Bond Liquidity before and after the Onset of the Subprime Crisis,” Journal of Economic Literature, Vol. 103, pp. 471–492.

22. N. Friewald, R. Jankowitsch, and M. Subrahmanyam, 2012, “Illiquidity or Credit Deterioration: A Study of Liquidity in the US Corporate Bond Market during Financial Crises,” Journal of Financial Economics, Vol. 105, pp. 18–36.

23. Other liquidity proxies include bond characteristics such as the amount issued, coupon, maturity, and age. In general, liquidity decreases with a bond’s age and maturity, but increases with its issu-ance size. These are static measures and will typically drop out of a panel regression model of spread changes.

Notes 179

24. The statistical significance of the zero-return measure is very low. It may not be meaningful measure.

25. Amy K. Edwards, Lawrence E. Harris, and Michael S. Piwowar, 2007, “Corporate Bond Market Transaction Costs and Transparency,” The Journal of Finance, Vol. 62, pp. 1421–1451.

26. A. Nashikkar, M. G. Subrahmanyam, and S. Mahanti, 2011, “Liquidity and Arbitrage in the Market for Credit Risk,” Journal of Financial and Quantitative Analysis, Vol. 46, Issue 3, pp. 627–656.

27. D. Duffie and K. J. Singleton, 2003, Credit Risk: Pricing, Measurement, and Management, Princeton, NJ: Princeton University Press.

28. Robert C. Merton, 1974, “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,” The Journal of Finance Vol. 29, pp. 449–470.

29. L. Chen, D. A. Lesmond, and J. Wei, 2007, “Corporate Yield Spreads and Bond Liquidity,” The Journal of Finance Vol. 62, No.1, pp. 119–149.

30. F. Longstaff, S. Mithal, and E. Neis, 2005, “Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default Swap Market,” The Journal of Finance, Vol. 9, No. 5, pp. 2213–2253.

31. Y. Amihud and H. Medelson, 1991, “Liquidity, Maturity, and the Yields on U.S. Treasury Securities,” The Journal of Finance, Vol. 46, pp. 1411–1425.

32. As an alternative, an investor could buy a string of short-term securi-ties that mature in sequence. Such a policy will, however, expose the investor to reinvestment risk because his horizon is larger than the longer of the securities’ maturity.


34. D. Duffie and K. J. Singleton, 2003, Credit Risk: Pricing, Measurement and Management, Princeton, NJ: Princeton University Press.

35. R. A. Jarrow and P. Protter, 2004, “Structural versus Reduced Form Models: A New Information Based Perspective,” Journal of Investment Management, Vol. 2, No. 2, pp. 1–10.

36. F. Longstaff, S. Mithal, and E. Neis, 2005, “Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default

180 Notes

Swap Market,” The Journal of Finance, Vol. 9, No. 5, pp. 2213–2253. D. Duffie and K. J. Singleton, 1999, “Modeling Term Structures of Defaultable Bonds,” The Review of Financial Studies, Vol. 12, Issue 4, pp. 687–720.

37. The default intensity is related to the risk-neutral probability of default. It is assumed that default occurs at the first arrival of a risk-neutral Poisson process whose intensity process is λ.

38. F. Longstaff, S. Mithal, and E. Neis, 2005, “Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default Swap Market,” The Journal of Finance, Vol. 9, No. 5, pp. 2213–2253.

39. R. Jarrow, 2001, “Default Parameter Estimation Using Market Prices,” Financial Analysts Journal, September/October.

40. This assumption allows us to separate the expectations of a product into a product of expectations.

41. D. Duffie, 1999, “Credit Swap Valuation.” Financial Analysts Journal, January/February.

42. The mathematical detail of this type of solution is beyond the scope of this book. The interested reader is referred to Appendix A in D. Duffie and K. J. Singleton, 2003, Credit Risk: Pricing, Measurement and Management, Princeton, NJ: Princeton University Press.

43. Note that the credit default swap premium may be a biased measure of the default component in corporate spreads. See D. Duffie, and Jun Liu, 2001, “Floating-Fixed Credit Spreads,” Financial Analysts Journal, Vol. 57, pp. 76–87.

44. P. Veronesi and L. Zingales, 2010, “Paulson’s Gift,” Journal of Financial Economics, Vol. 97, pp. 339–368.

45. Z. He and W. Xiong, 2012, “Rollover Risk and Credit Risk,” The Journal of Finance, Vol. 67, No. 2, pp. 391–428.

46. R. Merton, 1974, “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,” The Journal of Finance, Vol. 29, No. 2, pp. 449–470.

47. The firm’s assets are assumed to be a random process whose loga-rithm is normally distributed, which ensures that the assets in the model are nonnegative.

Notes 181

48. F. Black and M. Scholes, 1973, “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy, Vol. 81, No. 3, pp. 637–654.

49. F. Black and J. C. Cox, 1976, “Valuing Corporate Securities: Some Effects of Bond Indenture Provisions,” The Journal of Finance, Vol. 31, No. 2, pp. 351–367.

50. H. Leland and K. Toft, 1996, “Optimal Capital Structure, Endogenous Bankruptcy and the Term Structure of Credit Spreads,” The Journal of Finance, Vol. 51, No. 3, pp. 987–1018.

51. Z. He and W. Xiong, 2012, “Rollover Risk and Credit Risk,” The Journal of Finance, Vol. 67, No. 2, pp. 391–429.

52. The Poisson distribution is often used in finance to model the occur-rences of random events. The first practical application of the dis-tribution was in 1898, when Ladislaus Bortkiewicz was tasked with investigating the number of soldiers in the Prussian army who were accidentally killed by horse kicks.

53. He and Milbradt developed a version of the HX structural model in which market liquidity arises due to search and bargaining in the OTC market. See Z. He and K. Milbradt, 2012, “Endogenous Liquidity and Defaultable Bonds,” Working Paper, for a discussion on default bond pricing, and chapter 4 for a discussion on a search and bargaining model of market liquidity.

54. The interested reader is referred to the paper by Z. He and W. Xiong, 2012, “Rollover Risk and Credit Risk,” The Journal of Finance, Vol. 67, No. 2, pp. 391–429 for the details behind the derivation of these equations.

55. H. Chen, R. Cui, Z. He, and K. Milbradt, August 2014, “Quantifying Liquidity and Default Risks of Corporate Bonds over the Business Cycle,” Working Paper.

6 Financial Regulation and Liquidity Risk Management

1. P. L. Bernstein, 2005, Capital Ideas: The Improbable Origins of Modern Wall Street, Hoboken, NJ: John Wiley & Sons. Published simultane-ously in Canada.

182 Notes

2. V. W. Fang, T. H. Foe, and S. Tice, 2009, “Stock Market Liquidity and Firm Value,” Journal of Financial Economics, Vol. 94, pp. 150–169.

3. A. Bervas, May 2006, “Market Liquidity and its Incorporation into Risk Management,” Banque de France, Financial Stability Review, No. 8.

4. Calculations by L. Laeven and F. Valencia using data from the banking crisis database. See speech by S. Ingver, Chairman, Basel Committee on Banking Supervision and Governor, Sveriges Riksbank at the Federal Reserve Bank of Chicago, November 2014.

5. Speech by S. Ingver, Chairman, Basel Committee on Banking Supervision and Governor, Sveriges Riksbank at the Federal Reserve Bank of Chicago, November 2014.

6. SIFMA, US Research Quarterly, 2014, “Equity and Other Markets,” US Research Quarterly, Second Quarter.

7. Y. Amihud and H. Mendelson, 1991, “Liquidity, Asset Prices and Financial Policy,” Financial Analyst Journal, Vol. 47, No. 6, pp. 55–66.

8. K. Balakrishnan, M. B. Billings, B. Kelly, and A. Ljungqvist, 2014, “Shaping Liquidity: On the Causal Effects of Voluntary Disclosures,” The Journal of Finance, Vol. 69, No. 5, pp. 2237–2278.

9. E. Barreto, 2014, “Alibaba IPO Ranks as World’s Biggest after Additional Shares Sold,” Reuters, September 22.

10. For a discussion on the management of funding liquidity risk, see John. C. Hull, 2012, Risk Management and Financial Institutions, third ed., Hoboken, NJ: John Wiley & Sons. Published simultane-ously in Canada.

11. A. Bangia, F. Diebold, T. Schuermann, and J. Stroughair, 1998, “Modeling Liquidity Risk with Implications for Traditional Market Risk Measurement and Management,” Working Paper, The Wharton Financial Institutions Center.

12. Liquidity-adjusted VaR was proposed by A. Bangia, F. Diebold, T. Schuermann, and J. Stroughair, 1999, “Liquidity on the Outside,” Risk, No. 68. Our treatment follows that of John Hull. See John C. Hull, 2012, Risk Management and Financial Institutions, third ed., Hoboken, NJ: John Wiley & Sons. Published simultaneously in Canada.

Notes 183

13. A. Bervas, 2006, “Market Liquidity and Its Incorporation into Risk Management,” Banque de France, Financial Stability Review, No. 8.

14. Basel Committee on Banking Supervision, 2009, Guidelines for Computing Capital for Incremental Risk in the Trading Book, Trading Book Group of the Basel Committee on Banking Supervision, Bank of International Settlements, July.

15. D. Brigo and C. Nordio, 2010, “Liquidity-Adjusted Market Risk Measure with Stochastic Holding Period,” Working Paper.

16. N. Gârleanu and L. H. Pedersen, 2007, “Search-and-Matching Financial Markets: Liquidity and Risk Management,” AEA Papers and Proceedings, Vol. 97, No. 2, pp. 193–197.

17. A. Shleifer and R. Vishny, 2011, “Fire Sales in Finance and Macro-economics,” Journal of Economic Perspectives, Vol. 25, No. 1, pp. 29–48.

18. F. Duarte and T. M. Eisenbach, 2014, “Fire-Sale Spillovers and Systemic Risk,” Federal Reserve Bank of New York Staff Reports, no. 645, October 2013; rev. May 2014.

19. M. K. Brunnermeier and L. H. Pedersen, 2008, “Market Liquidity and Funding Liquidity,” The Society for Financial Studies, pp. 2202–2238.

20. Fire sales are not limited to crisis periods. For example, poorly per-forming mutual funds without significant cash reserves may also be forced to sell holdings quickly. Harvard University professors Joshua Coval and Eric Stafford looked at the effect on the drop in stock prices when mutual funds with holdings of a particular stock are forced to sell due to investor withdrawals. See J. Coval and E. Stafford, 2007, “Asset Fire Sales (and Purchases) in Equity Markets,” Journal of Financial Economics, Vol. 86, pp. 479–512.

21. Z. He, I. G. Khang, and A. Krishnamurthy, 2010, “Balance Sheet Adjustments during the 2008 Crisis,” International Monetary Fund Economic Review, Vol. 58, pp. 118–156.

22. H. Till, 2006, EDHEC Comments on the Amaranth Case: Early Lessons from the Debacle, EDHEC Risk and Asset Management Research Centre.

23. One of the factors in the Liquidity Adjusted Capital Asset Pricing discussed in chapter 4 quantifies this particular component of mar-ket liquidity.

184 Notes

24. Corporate liquidity risk management usually involves matching cash flows using a gap analysis, which is a related but different aspect.

25. D. Bisias, M. Flood, A. Lo, and S. Valavanis, 2012, “A Survey of Systemic Risk Analytics,” US Department of Treasury, Office of Financial Research, Working Paper.

26. Final Rule and Interpretive Guidance to Section 113 of the Dodd-Frank Consumer Protection Act.

27. The Group of Ten members was established in 1974 and it included 12 countries: Belgium, Canada, France, Germany, Italy, Japan, Luxemburg, the Netherlands, Sweden, Switzerland, United Kingdom, and the United States.

28. Bank of International Settlements, 2014, “A Brief History of the Basel Committee,” Basel Committee on Banking Supervision, BIS.

29. Empirical research showed that funding liquidity and market liquidity are different but related concepts. Reduced funding liquidity can cause market liquidity and vice versa. See M. K. Brunnermeier and L. H. Pedersen, 2009, “Market Liquidity and Funding Liquidity,” Review of Financial Studies, Vol. 22, No. 6, pp. 2201–2238.

30. The discussion and examples provided in the sections titled “The liquidity coverage ratio” and “The net stable funding ratio” are from similar discussions by John Hull, see John C. Hull, 2012, Risk Management and Financial Institutions, third ed., Hoboken, NJ: John Wiley & Sons. Published simultaneously in Canada.

31. International Monetary Fund, April 2011, “How to Address the Systemic Part of Liquidity Risk,” Global Financial Stability Report, Chapter 2.

32. Wall Street Journal, 2014, “U.S. Regulators Tweak Final Liquidity Rule for Large Banks,” September 3.

33. Wall Street Journal, 2014, “U.S. Regulators Tweak Final Liquidity Rule for Large Banks,” September 3.

34. Blackrock Investment Institute, September 2012, “Got Liquidity?” Blackrock, http://www.blackrock.com/investing/literature/whitepaper /got-liquidity-us-version.pdf



Notes 185

35. W. Bagehot, [1873] 1999, Lombard Street: A Description of the Money Market, London: King report; New York: Wiley.

36. J. C. Stein, 2013, “Liquidity Regulation and Central Banking,” Remarks by Jeremy C. Stein, Member, Board of Governors of the Federal Reserve System, 2013 Credit Markets Symposium.

37. Western Union Internal Memo, 1876, Quotes on Value.

AAA bonds, 21, 23, 118, 122, 126ABS. See asset-backed securities (ABS)ABX, 31Acharya, Viral (professor), 101, 103Acharya-Pederson pricing model, 101–3Acropolis (Athens), 3adverse markets, 92adverse selection, 9, 45–6, 48, 53

in dealer markets, 85limit orders and, 57, 60–1liquidity and, 32

after-the-fact intervention, 161agency models, 93–101, 108agency notes, 26aggregate market risks, 12, 75aggregate supply vs. aggregate demand, 5–6agora (ancient Greek market), 3Akerlof, George (Nobel laureate), 44Alibaba (company), 147alternative trading platforms, 46

Regulation ATS, 51, 62–3Amaranth (hedge fund), 154American Finance Association, 68, 83American Sands Energy Corporation

(AMSE), 61American Society of Finance, 84Amihud, Yakov (professor), 17, 78, 84, 126Amihud measure, 84, 107–8, 124Amihud-Mendelson model, 78antiquity, 2–3, 6Apple (company), 6, 44, 46arbitrage. See no arbitragearbitrage opportunities, 11–12, 91–2, 94arbitrage portfolio, 95arbitrageurs, 92–100, 173n58, 175n20

access to capital of, 11CDS-bond basis and, 68–70in frictionless economy, 33principal/agent problem and, 34

Argentina, 117Arrow, Kenneth (economist), 6Arrow-Debreu model, 6–7artificial intelligence, 41ask prices. See bid and ask pricesask quotes, 47, 50, 53asset pricing models. See under specific modelsasset-backed commercial paper (ABCP), 5, 24,

27–8

Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility (AMLF), 24

asset-backed securities (ABS), 20, 44, 48, 118ABCP and, 27–8during crisis, 144margins on, 153

assets. See under specific assetsasymmetric information, 31–2, 44–6, 61, 76

Bagehot on, 9commonality in liquidity and, 105in dealer markets, 85in equity markets, 136financial disclosures and, 146Gorton on, 20on OTC markets, 86

asymmetric information costs, 61asymmetric information model, 169n14Athens, 3auctions, 50–1, 57–8

Treasury, 66, 91Walrasian, 7–8, 39, 41–2, 60–1See Walrasian auction

autocorrelations, 83automated trading, 40–1, 61, 63–6available stable funding factor (ASF), 158

Bagehot, Walter (essayist), 9, 161bail outs, 22, 136balance sheets, 12, 70–1, 117–18

vs. ABCP, 27Basel III and, 158–61liquidity of, 32repurchase agreements and, 29, 33–4risk weight of, 145

bank accounts, risk-free, 87bank asset risk, 7Bank of America, 58Bank of England, 2bank portfolios, 32bank-based systems, 4–5bankers, 3, 21bankruptcy, 27, 134, 138. See also Lehman Brothers

bankruptcy (2008); Long-Term Capital Management (LTCM) bankruptcy (1998)

bankscommercial, 20, 67, 152–4as financial intermediaries, 6–7interbank markets, 20, 30–2, 120, 178n14

Index

188 Index

banks—Continuedinvestment, 20, 22, 29, 67, 146–7See also central banks; specific banks

Bao, Jack (professor), 122–3, 127Barclays LX (ATS), 63bargaining power, 55–6, 59, 119

in search-and-bargaining model, 86, 89–91, 110, 113

barter, 2–4Basel Accord, 67Basel Committee on Banking Supervison, 151,

155–6Basel III (regulation), 70, 145, 155–60basis, 34, 76, 173n58

CDS-bond, 68–70, 118–20See also yield spreads

basis points, 69, 76, 115, 122, 139–40Bayesian learning process, 43BBB bonds, 126Bear Stearns, 72, 160behavioral economics, 77, 93Bernake, Ben (chairman of Federal Reserve), 1, 35Bernstein, Peter (author), 143Bervas, Arnaud, 144bespoke securities, 48–9Biais, Bruno, 58bid and ask prices, 53–4, 80–1

in equilibrium price, 42–3market makers and, 89–90on NASDAQ, 51on OTC markets, 85in search-and-bargaining model, 88–9, 109–11

bid quotes, 47, 50, 53bid-ask spreads, 19, 37, 42–4, 54–8, 108

asset pricing and, 78–83bargaining power and, 113basis trading and, 120Black on, 9–10as compensation, 85frequency of transactions and, 60high-margin securities and, 71–2information symmetry and, 45–6LCAPM and, 101liquidity and, 119in portfolio risk, 147public listing and, 146in Roll measure, 122in search-and-bargaining model, 77, 86, 88–91Stoll on, 61–2transaction costs and, 135VaR and, 148–51

bilateral trade, 40, 54–6Black, Fisher (economist), 9, 16, 134Black-Scholes option pricing model, 9, 12, 33, 134Bloomberg (real-time feed), 46–7, 54bond indices, 117

bond markets, 2, 33, 48, 130, 178n14corporate, 53–4, 70, 120, 125, 141liquidity in, 115–27, 133–6, 139, 141

bond prices, 12bond rating, 125bond risk premium, 36bond yield spread, 139bond-credit default swap basis. See credit

default swap (CDS)-corporate bond basisbonds

consol, 87, 109, 174n16defaultable, 116, 125, 127–32, 141government, 2, 76, 115, 157investment-grade, 34, 69–70, 116, 125–6,

139–41long-term, 125–6municipal, 48, 56securitized, 21zero-coupon, 93–4, 127, 130, 133See also corporate bonds; Treasury bonds

Bortkiewicz, Ladislaus, 181n52boundary conditions, 138Bretton Woods system, 4, 156Brigo, Damiano (researcher), 151brokerage firm analysts, 116brokerage frees, 78broker-dealers, 52, 54, 63, 152–3, 161brokers, 47, 54, 59, 62, 67–8broker-speculators, 67Brownian motion, 129–30, 136Brunnermeier, Markus (professor), 71–2, 152–3buy and sell orders, 43–4, 51, 57, 59–60, 65

arbitrage and, 97–100dark pools and, 62in Roll measure, 81–3in search-and-bargaining model, 110

buy side, 54, 56, 85buy-and-hold investors, 28buying market, 2

call auctions, 58capital, 4, 33–7, 66–71

access to, 7, 11arbitrage and, 91–7, 99–101, 108of banks, 172n54bonds and, 134credit risk and, 117in frictionless market, 75interventions to, 161market structures and, 41in NSFR, 158in repurchase agreements, 29–30risk and, 154, 156withdrawal of, 45

capital asset pricing model (CAPM), 16, 101–3capital costs, 70, 143, 145–6

Index 189

Capital Ideas (Bernstein), 143capital investments, 10, 29capital markets, 1, 6–7, 37, 158capital structure of firms, 128, 135capitalization, 83Carney, Mark (governor of Bank of England), 2cash, 24–7, 31, 66–7, 153

as daily liquid asset, 25as high-quality liquid asset, 157, 159–60liquid bankers and, 21See also money

cash equities, 49, 65cash flows, 33, 127, 136–7, 161, 184n24

in absolute pricing, 12in asset pricing model, 131in classical pricing theory, 13during crisis, 144in Diamond coconut model, 87in discounted cash flow model, 79–80,

117–18expected, 5, 15liquid assets and, 35–6in search-and-bargaining model, 111–12in Shleifer-Vishny model, 94–5short-term, 37in traditional asset pricing, 75of Treasury bonds, 91

cash holdings, 29cash outlays, 145cash securities, 33cash-in-the-market pricing, 37Center for Research in Security Prices (CRSP)

databases, 176n1central banks, 1, 40, 155–6, 158

Basel III and, 160–1interbank markets and, 30, 32

central clearing, 40, 49, 73central order books, 51centralized markets, 3, 50, 55, 116

illiquidity costs in, 59limit order markets and, 57

cheapest-to-deliver option, 120Chen, Long (professor), 125–6Chicago, Illinois, 64Chicago Board of Trade, 40Chicago Board Options Exchange Volatility

Index (CBOE VIX), 127China, 147Chi-X (multilateral trading facility), 46Citadel Investment Group, 154Citi Match (ATS), 63Citigroup, 147classical finance theory, 39, 161–2classical pricing theory, 13Clearing House Interbank Payments System

(CHIPS), 40

CLS Bank, 40CME Globex platform, 59CME Group, 50–1, 59, 64coins, 2–3collateral, 20, 26–7, 55, 92

in interbank markets, 31land as, 3–4for lender of last resort, 161margins on, 157in repo agreement, 153repurchase agreements and, 29–30for Treasury bonds, 76

collateralized borrowing, 66–8, 71–2collateralized debt markets, 22collateralized debt obligations (CDOs), 5, 21, 48, 118

markets for, 49–50, 116collateralized mortgage obligations, 50commercial banks, 20, 67, 152–4commercial papers, 67, 157

asset-backed, 5, 24, 27–8commission, 143Committee on Banking Regulations and

Supervisory Practices, 151, 155–6commodities, 5–6, 49, 64common risk factors, 17commonality

of exposures, 155in liquidity, 101, 103–8, 126–7

comparable investments, 12competition, 45–6, 51–4, 59, 142

in financial sector, 5in Walrasian auction, 8

complete markets, 17, 44computers, 40–1, 58confidence level, 147, 149consol bonds, 87, 109, 174n16Constantinides, George (professor), 78consumption, 6–7, 13–16, 36, 154, 169n10consumption-based models, 16continuation value of firms, 26continuous trading, 58control systems, 144convertible bond arbitrage, 33corporate bond markets, 53–4, 70, 120, 125, 141corporate bond-credit default swap basis,

68–70, 119–20corporate bonds, 33–4, 47, 66, 115–30

Basel III and, 161CDS-bond basis, 68–70, 119–20default risk in, 76in LCR, 157liquidity of, 72on OTC markets, 50, 116risk premium on, 36, 115TRACE and, 170n26yield spreads for, 119, 122–6

190 Index

corporate debt, 116, 118corporate finance, 12corporate policies, 144corporate securities, 36corporate valuation, 133, 143–4correlation risk, 36costs. See under specific types of costcounterparties, 70, 147, 157

finding of, 55–6, 62in OTC markets, 77, 85–6

counterparty default, 20, 26counterparty risk, 31–2, 34, 69, 120, 156coupon bonds, 131coupon payments, 76, 136–8Coval, Joshua (professor), 183n20Cox, John (economist), 134Cox-Ingersoll-Ross (CIR) model, 129creative destruction, 66credit, 55, 99, 133, 161

bonds markets and, 118, 122as margin, 66–7in reduced-form model, 129in regression model, 124

credit card receivables, 31credit default swap (CDS)-corporate bond

basis, 68–70, 119–20credit default swaps, 33–4, 116, 119–22, 133,

180n43as proxy for default risk, 125

credit exposure, 33–4, 49–50credit lines, 4–5, 28, 31, 67credit ratings, 21, 67, 86, 139credit risks, 23, 68, 115–16, 156

in bonds market, 117–20, 122, 125, 127, 142of defaultable bonds, 128, 132in HX model, 134–5, 139liquidity risks and, 151, 161in reduced-form model, 133in regression model, 123

credit spreads, 116, 140–1Credit Suisse Crossfinder (ATS), 63, 147credit-gap indicators, 155creditworthiness, 36, 56, 115crisis periods, 92–3, 99–100, 106, 161

fire sales and, 152–5, 183n20in liquidity, 19, 56, 152–3, 154See also global financial crisis (2008)

currency market, 54current markets, 10customer limit order markets, 58

daily liquid assets, 25–6daily transaction prices, 84dark pools/dark ATS, 62–3De Soto, Hernando (economist), 4dead capital, 4dealer banks, 40, 67, 70

dealer markets, 48, 50–6, 85dealers, 1, 8, 52–6, 62–3

bargaining power of, 89in bid-ask spreads, 82, 135broker-dealers, 152–3, 161capital requirements of, 67–8, 70at NASDAQ, 42in search-and-bargaining model, 85–6, 110See also financial intermediaries; market

makersDebreu, Gerard (economist), 6debt, 67, 116–18, 133–7, 141–2

ballooning, 70–1capital and, 143, 153–4credit risk and, 128, 133equity and, 156–7in HX model, 139long-term, 26market, 50short-term, 27–8, 36See also collateralized debt obligations (CDOs)

debt market, 20, 105, 117default, 23, 27, 36, 158

in asset pricing model, 131–3on corporate bonds, 68, 137counterparty, 20, 26credit risk and, 127liquidity and, 117, 161master agreements and, 55in option-pricing model, 134recovery payments and, 138in reduced-form model, 128–9See also Lehman Brothers

default boundary, 134, 141default intensity, 129, 132, 180n37default losses, 173n57default premium, 117, 138, 141default risks, 23, 28, 32, 36

in asset pricing, 76for corporate bonds, 141–2risk premium and, 115yield spread and, 125–6

defaultable bonds, 116, 125, 127–32, 141delayed feeds, 47delevering, 153demand, 98–9, 109, 153

for cash, 31for low-risk assets, 154for repo agreements, 29See also supply and demand

demand deposits, 6, 25, 67Demsetz, Harold (economist), 7, 42, 59, 61Depository Trust Company, 40deposits, 1, 4–7, 29–31, 157–60derivative market, 117–18, 120derivatives, 33–4, 48–50, 58

OTC, 40, 55

Index 191

Deutsche Börse (German stock market), 46, 50–1, 147

Diamond, Peter (Nobel laureate), 86–8Diamond coconut model, 86–8, 174n15direct obligations, 26discount cash flow model, 79–80, 117–18discount factors, 12–17, 75discount rate, 131, 137–8discounted future value, 80, 111–12discounted present value, 127dislocations, 19–20, 31, 33, 144dissemination, 47–8, 52, 72–3dividends, 78–9Dodd-Frank Act (2010), 155Dow Jones Industrial Average, 169n12drachmas, 2Duffie, Darrell (professor), 55–6, 68, 77, 85–6

Easley, David (professor), 43Economic Club of New York, 35Economic Sciences, 4, 7economic theory, financial, 32Economist (magazine), 1–2effective spreads, 81efficient market hypothesis, 10efficient markets, 10–11, 41, 44–5, 65, 94electronic access networks, 40, 53electronic communication networks (ECNs), 51–3electronic limit order books, 47, 50–1, 58–9electronic markets, 52, 63–4, 66electronic trading systems, 40, 57–8, 64e-minis, 2, 64end-of-day prices, 121–2endowments, 92–3equilibrium, 6–13, 36, 45, 56, 60

in Amihud-Mendelson model, 78arbitrage and, 98–100between supply and demand, 75

equilibrium prices. See fundamental valueequity, 17, 64–5, 76, 139, 145–6

in asset pricing, 6, 133–4capital and, 143, 153cash, 49credit risk and, 128debt and, 156fire sales and, 154of homes, 5in HX model, 134–5issuance of, 36limit order markets and, 58on OTC markets, 50in S&P 500 index, 92trades in, 40–1

equity capital, 67, 92, 153equity exchanges, 52–3equity markets, 52, 65, 117–18, 136Eurodollars, 50

Euronext (stock market), 46, 50–1, 59Europe, 46, 117European Central Bank, 31European Markets in Financial Instruments

Directive (“MiFID” 2007), 64event-based time, 64exchange rates, 4, 156exchanges, 40, 47, 50–3, 59, 83

costs of, 145dark pools and, 62in National Market System, 64vs. OTC markets, 85

exchange-traded environment, 53, 85, 119exchange-traded funds (ETFs), 2, 5execution costs, 48, 55–6, 81, 85execution prices, 50, 56–8, 80

in dark pools, 63in liquid markets, 8–9in Walrasian market, 60–1

exogenous costs, 61, 75, 78, 101, 107–8exogenous risk, 36exogenous shocks, 94, 134exotic derivatives, 48expected future rollover effects, 134–5expected returns, 83, 92–3, 136–7, 145

in CAPM, 16–17, 102, 105liquidity and, 34–5, 79

exposure, credit, 33–4, 49–50

Facebook, 59failure of no-arbitrage principle, 77.

See also no arbitragefair value, 11Fama, Eugene (Nobel Prize winner), 11Fang, Vivian (professor), 143–4fear gauge, 127federal government agencies, 26federal insurance, 23Federal Reserve, 1, 11, 35, 66–7, 161

ABCP market decline and, 28AMLF, 24

Federal Reserve Banks, 24, 40, 152Fedwire, 40finance theory, 32, 39, 41, 77, 161–2financial collapse in Southeast Asia, 92financial crisis. See global financial crisis (2008)financial disclosures, 146financial economics, 9Financial Industry Regulatory Authority

(FINRA), 48financial intermediaries, 6, 21–2, 48, 53, 70

as auctioneers, 39bid-ask spreads and, 45–6, 50capital requirements and, 41fire sales and, 153inventory risks of, 105on OTC markets, 85

192 Index

financial intermediaries—Continuedrisk aversion and, 154shocks and, 37See also dealers; market makers

financial markets, 1, 4–11, 30, 91–2disruptions in, 155–6in global crisis, 19rollover risk and, 141–2

Financial Times (newspaper), 32financing costs, 55, 87fire sales, 21, 26–7, 105, 144–5

crisis and, 152–5, 183n20First Investment Trust (index fund), 5first-passage models, 134Fisher, Lawrence (professor), 36, 115, 176n1fixed cost, 6fixed-income futures, 66fixed-income markets, 2, 177n9fixed-income portfolios, 147–8fixed-income securities, 70–1, 118fixed-income trading, 65–6, 116flash crisis (May 6, 2010), 1–2, 169n12flight-to-quality phenomenon, 141floating currencies, 4floor-based exchange trading, 40, 51, 57–8, 64foreign exchange, 53, 64, 86, 116–17for-profit entities, 51Foucault, Thierry (professor), 58, 104fragmentation of markets, 7, 62–3, 65–6France, 46Francis Emory Fitch, Inc., 121Frankfurt Bourse, 59free market economics, 4freezes, 22, 31–2frictionless markets, 5–6, 33, 75, 134–5

CAPM and, 101CDS-bond basis in, 68risk management and, 147–8Shleifer-Vishny model and, 94Walrasian auction as, 8

frictions, 8, 33, 89in equity markets, 136in OTC markets, 119real-world, 6, 35

Friedman, Milton (economist), 4Friewald, Nils (professor), 123–4fund managers, 56, 93–5

hedge fund, 11, 13, 35, 121mutual fund, 47, 94

fund portfolios, 24fund sponsors, 23fundamental returns, 82fundamental securities, 33fundamental threshold, 134fundamental value (general equilibrium price),

7–8, 14–16, 39, 88–90, 130

arbitrage and, 91, 93–4, 96–8, 100asset pricing and, 19, 75, 79–80in HX model, 134–5, 139liquidity premium and, 127–8in modified Roll measure, 121in no arbitrage, 10–11, 33–4risk calculation and, 148in Roll measure, 82–3of security, 42, 44

funding, 6, 22, 27, 99–100funding liquidity, 20, 28–9, 160, 184n29

market liquidity and, 37, 72, 152, 154future contracts, 11future value, 6futures, 11, 17, 40–1, 48, 64–6

Eurodollar, 50

Gârleanu, Nicolae (professor), 34, 85–6Gaussian normal distributions, 149general equilibrium economy, 6–8. See also

equilibriumgeneral equilibrium price. See fundamental valueGermany, 46Getco (ATS), 63global financial crisis (2008), 19–36, 140, 144–5,

160–2bonds markets and, 117–18, 122, 127capital during, 68–70illiquidity in, 1–2, 156origins of, 152–3

global markets, 4, 59, 144globalization, 41, 58Glosten, Lawrence (professor), 45, 58, 61gold standard, 3–4Goldman Sachs, 54, 63, 147Gorton, Gary (Yale professor), 20, 29–31, 167n20government bonds, 2, 76, 115, 157government data releases, 66government guarantees, 144government-sponsored enterprises, 26Great Depression, 106Greenspan, Alan (Chairman of Federal

Reserve), 66gross domestic product (GDP), 144gross market returns, 105gross yield, 23Grossman, Sanford (economist), 9Group of Ten (G10), 155–6, 184n27

Hagströmer, Björn (professor), 106, 176n33haircuts. See marginsHansson, Björn (professor), 106, 176n33He, Zhiguo (professor), 134–5, 139, 181n53hedge fund managers, 11, 13, 35, 121hedge funds, 20, 67–9, 92, 94

arbitrage strategies of, 33, 92

Index 193

collapse of, 153–4See also Long-Term Capital Management

(LTCM) bankruptcy (1998)hedgers, 12hedging, 55, 85, 105, 119, 147hedging needs, 48heteroskedasticity, 129Hicks, John Richard (economist), 4, 9high rollers, 87–9, 109, 111high-frequency trading, 40–1, 61, 63–6high-margin securities, 71high-quality liquid assets, 157high-speed alternative trading systems

(ATSs), 63holding costs, 42–3, 87–9, 113, 119holding periods, 77–8, 89, 106, 148, 151

of bonds, 126Holmstrom, Bengt (MIT professor), 27homes, 5, 40, 85, 143horizon effect, 121hot potato trading, 1–2households, 54HX model, 135–9, 181n53

idiosyncratic risks, 14–15IGT Posit (ATS), 63illiquid markets, 8, 117, 130, 144, 156illiquidity costs, 59illiquidity discount (liquidity affect),

37, 96, 100immediacy, 7–10, 42, 61–2, 68imperfect searches, 85index funds, 5, 92index pop, 76inefficiencies of markets, 65information, 34–5, 43–7, 53–7, 63–6, 169n10

acquiring of, 6market structures and, 50price formation and, 39public vs. private, 41symmetric, 146value of security and, 82in Walrasian auction, 8See also asymmetric information

information costs, 3, 48, 61initial public offerings (IPO), 147insider trading, 53insolvency, 29, 156

solvency, 30, 133, 160–1Instinet (ATS), 63institutional arrangements, 41, 44, 49, 50, 55institutional cash pools, 27institutional fund managers, 56institutional investors, 40, 52, 56, 92, 146

in dark pools, 62high-frequency trading and, 66

insurance, 14, 15, 23, 25, 68deposit, 7title, 40

interbank markets, 20, 30–2, 120, 157, 178n14interdealer markets, 54, 86–7, 110interest rates, 26, 85–7, 90, 120, 147–8

default risk and, 129interbank markets and, 32liquidity premium and, 117uncertainty of, 127

intermediaries. See financial intermediariesInternational Monetary Fund (IMF), 144International Swap and Derivative

Association (ISDA), 55intertemporality, 8inventories, 55–6, 70, 105, 161

in Diamond coconut model, 87in high-frequency trading, 64in price formation, 42–3in search-and-bargaining model, 85–6, 110

investment accounts, 46investment banks, 20, 22, 29, 67, 146–7Investment Company Act, 23–4investment horizon, 126investment managers, 85, 93investment-grade bonds, 34, 69–70, 116, 125–6,

139–41investors. See institutional investorsITS order routing system, 64

J.P. Morgan, 69, 72, 147, 154Jankowitsch, Rainer (professor), 123–4Jarrow, Robert (professor), 130Journal of Finance, 146Journal of Financial Markets, 58

Keynes, John Maynard (economist), 4, 9, 19Knight (ATS), 63Kyle, Albert (professor), 43, 84, 108

land, 3–4latency issues, 65latent liquidity risks, 19–20, 27, 32law of one price, 10, 91learning problem, 43least-square optimization procedure, 132Lehman Brothers bankruptcy (2008), 23, 30–2,

107, 157, 160bonds market and, 115Reserve Primary Fund and, 20

Leland, Hayne (professor), 134lender of last resort, 161Lesmond, David (professor), 125–6level premium, 104, 106–7leverage, 29, 35, 91–2, 139–40, 153

Basel III and, 156

194 Index

liabilities, 26, 67, 71, 128, 154Basel III and, 160

Libor-OIS spread, 31limit orders, 42, 47, 50–1, 57–61, 171n31

in dark pools, 63Roll measure and, 81

limit price, 58linear models, 123liquid asset basket, 25–6liquid bankers, 21liquid markets, 8–10, 35, 37, 120, 124

bid-ask spreads in, 45, 80mean-variance framework and, 101NYSE as, 52price impact and, 84reliance on, 143

liquidation, 92, 95–7, 100–1, 105crisis and, 151of LTCM, 115portfolio value and, 147–8of SIFIs, 155transaction costs and, 149

liquidation costs, 126, 141liquidation price, 148liquidation value, 138liquidity backstop, 28liquidity costs, 106, 119, 126, 148, 150liquidity coverage ratio (LCR), 155–7liquidity crises, 19, 56, 152–3, 154liquidity effect (illiquidity discount), 37, 96, 100liquidity level premium, 104, 106–7liquidity premium, 17, 96–100, 116–20, 174n17

of bonds, 141, 161during crisis, 144fundamental value and, 127–8in HX model, 135, 138–9LCAPM and, 104, 106in reduced-form model, 133in search-and-bargaining model, 86, 88–9

liquidity profiles, 17, 48–9liquidity proxies, 178n23liquidity risk premium, 15, 103–7, 154, 176n33

on corporate bonds, 115, 117, 119liquidity risks, 19–23, 26–8, 144–5, 150–2, 155–7

of asset, 12, 77of bonds, 126–7, 131, 136, 141in CAPM, 103–7CDS and, 120credit risk and, 151, 161exogenous vs. endogenous, 148funding and, 37latent, 19–20, 27, 32in mean-variance framework, 101in reduced-form model, 129systemic, 101, 160See also risk management

liquidity shocks, 20–1, 134–7, 139–41liquidity suppliers, 47, 59, 62, 65liquidity-adjusted capital asset pricing model

(LCAPM), 77, 101–4, 106–8, 176n33liquidity-adjusted value-as-risk (VaR), 148–51liquidity-provision policies, 141Liquinet (ATS), 63listed derivatives, 49lit markets, 63live capital, 4loans, 3–5, 27–9, 67, 159–61

as assets, 17illiquidity and, 35in interbank markets, 31timing risks and, 91–2

loan-to-value ratios, 5London Bourse, 59London International Financial Futures and

Options Exchange (LIFFE), 50–1London Stock Exchange (LSE), 50–1Longstaff, Francis (professor), 35, 76, 125, 129–30long-term assets, 28, 125–6, 156long-term borrowing, 67Long-Term Capital Management (LTCM)

bankruptcy (1998), 17, 92, 105, 107, 115as liquidity spiral, 153–4

long-term debt, 26long-term funding, 158long-term investors, 50low rollers, 87–8, 109, 111Lydians, 2–3

macroeconomy, 12, 117–18, 125, 155managers. See under specific managersmanual markets, 62margin calls, 37, 92margins (“haircuts”), 29–30, 33–4, 66–72, 139

arbitrage and, 91on corporate bonds, 70crisis and, 153, 157repo, 167n20

market capitalization, 83market depth, 9, 45, 84, 119, 147market efficiency, 41, 93market impact measures, 84market liquidity risks. See liquidity risksmarket makers, 7–9, 78, 84, 110–11

access to capital of, 72as auctioneers, 39, 42competition between, 45in dealer markets, 54in Easley and O’Hara setup, 43electronic, 40, 63human, 65information from, 47on NASDAQ, 51

Index 195

net capital rule and, 67–8on OTC markets, 50, 70, 85–6, 177n9in search-and-bargaining model, 87–91underwriters as, 147See also dealers; financial intermediaries

market meltdowns, 17market microstructure, 9, 45, 84, 108, 119, 155Market Microstructure (Biais, Glosten, and

Spatt), 58market orders, 57–61, 63, 171n31. See also buy

and sell orders; limit ordersmarket portfolios. See portfoliosmarket power, 47, 59, 88market returns, 16, 78, 101, 104–6.

See also returnsmarket structures, 5–7, 50–3, 64–5, 83

adverse selection and, 46of dealer markets, 56liquidity and, 44–5, 60, 143of OTC markets, 57, 116technological advances and, 40–1, 50

market value, 55, 61, 95, 133of bonds, 115, 135

marketabilityof bonds, 115of financial instruments, 143of security, 36

market-based prices, 25market-clearing prices, 42, 58, 60, 98–9, 102market-to-book value ratios, 144master agreements, 40, 55matching engine protocols, 65maturity mismatch, 158maturity security, 126maturity structure, 26–7McCabe, Patrick (economist), 23mean-variance framework, 77, 101medium-term funding, 158Mendelson, Haim (professor), 78Merchant of Venice (Shakespeare), 3Merton, Robert (Nobel laureate), 7, 116, 125Merton model, 116, 133Metrick, Andrew (Yale professor), 29–30, 167n20midprices, 81, 83, 147–50midquotes, 79, 81–3mid-term notes, 27Milbradt, Konstantin, 181n53Milgrom, Paul (professor), 45Millennium Bridge of London, 36–7Miller, Merton (Nobel laureate), 9minting of coins, 3mispricing, 11, 20, 33, 57–8

arbitrage and, 91–100Mitchell, Mark (professor), 33Mithal, Sanjay, 129–30modernization, 143

modified Roll measure, 121, 127monetary policies, 1, 4, 30, 117–18money, 1–5, 32, 67, 115, 151

arbitrage and, 91–4as liquid asset, 17, 39substitutes for, 4–5time value of, 11, 13, 127See also cash

money managers, 34, 94money market funds (MMFs), 5, 19–20,

22–6, 40, 183n20Monto Carlo simulation, 149Morgan Stanley MS Pool (ATS), 63mortgage-backed securities (MBS), 5, 27,

32, 144, 153mortgages, 27–8, 40, 157, 160

illiquidity and, 35origins of, 164n15as stores of value, 5

multicolinearity, 123multilateral trading facility (MTF), 46multiplier effect, 151municipal bonds, 48, 56mutual fund managers, 47, 94mutual funds, 5, 19–20, 22–6, 40, 183n20

National Association of Securities Dealers Automated Quotations (NASDAQ), 42, 51–5, 65, 145

dealers on, 67–8Facebook on, 59vs. NYSE, 61–2, 83

National Association of Security Dealers (NASD), 116

National Market System (NMS). See Regulation National Market System

negative basis, 34Neis, Eric, 129–30Nelson, Bill (economist), 161neoclassical financial edifice, 2net asset value (NAV), 22–4net capital rule, 67Net Stable Funding Ratio (NSFR), 156, 158–9New York City, New York, 35, 64New York Stock Exchange (NYSE), 47–8, 50–3,

67, 106, 145electronic vs. floor trading at, 58–9vs. NASDAQ, 61–2, 83

New York Times (magazine), 121Niederhoffer, Victor, 121Nilsson, Birger (professor), 106, 176n33Nixon, Richard (US president), 4no arbitrage, 10–11, 32–5, 91–5, 130

asset pricing and, 77CDS-bond basis and, 120See also arbitrageurs

196 Index

Noe, Thomas (professor), 143–4noise traders, 43, 84no-load index mutual funds, 5nonasset-backed commerical papers, 28nonexecution, 57nonlisted securities, 51nonmarketability of assets, 35–6nonzero basis, 34Nordio, Claudio (researcher), 151Northern Rock, 157NYSE Arca all-electronic exchange, 58–9

October 1987 crash, 107off-balance entities, 28off-balance sheet items, 158off-the-run bonds, 91O’Hara, Maureen (professor), 43oil crises (1973, 1979), 107one-period arithmetic returns, 16on-the-run bonds, 91opacity, 44, 47, 54open access marketplaces, 59open-ended investment companies, 24operational charges, 67opportunity costs, 55, 63, 77option price, 9, 12, 33, 133–4option valuation, 12, 135order arrival process, 83order books, 9, 121. See also limit ordersorder flows, 42–4, 46–7, 50–1, 64–5, 83–4

asymmetric information and, 169n14in limit order markets, 61price and, 54, 121

order processing costs, 53, 61Ornstein-Uhlenbeck stochastic process, 129–31overnight financing, 29over-the-counter (OTC) derivatives, 40, 55over-the-counter (OTC) markets, 28, 35, 47–50,

85–6adverse selection in, 60Basel III and, 161bid-ask spreads in, 89vs. centralized markets, 116vs. exchange-traded environments, 53, 85, 119fixed-income markets and, 177n9in HX model, 181n53interbank markets and, 178n14structure of, 57trading in, 47–8, 70, 77

Pan, Jun (professor), 122–3, 127paper companies, 27payoffs, 10, 12–15, 75, 80

in CAPM, 103of convertible bonds, 33limit orders and, 60

Pedersen, Lasse (professor), 34, 71–2, 85–6, 151on asset pricing, 101, 103, 105on margins and crisis, 152–3

pension funds, 92, 94perfectly liquid markets, 8, 44, 75, 79, 101Peru, 4pledgability of assets, 27Poisson occurrence, 135, 180n37, 181n52policies, 53, 66, 161

corporate, 144–6during financial crisis, 19liquidity-provision, 141on MMFs, 23monetary, 1, 4, 30, 117–18pricing, 9regulatory, 40, 71–3, 117–18, 142See also regulations

portfolio managers, 85, 119, 147portfolio risks, 20, 23–4, 147, 150, 154portfolio securities, 24–5portfolios, 32–3, 95, 127, 141

in CAPM, 16CDS-bond basis in, 69of firms, 136of LTCM, 92, 105risk premium and, 103VaR of, 147–51

posttrade transparency, 47–8, 170n26premium. See liquidity premium; liquidity risk

premiumpretrade transparency, 47price changes, 2, 9, 19, 120–1

decrease, 42, 76order flow and, 44transaction volume and, 84

price concessions, 55, 61price discounts, 92, 97, 116price discovery, 7–8, 54, 60–3, 66

in asset pricing models, 17transparency and, 47

price dispersion measure, 124price formation, 7–8, 39, 41–4, 73, 169n14

in dark pools, 63high-frequency trading and, 66investors and, 60on OTC markets, 85

price impact, 9, 37, 84, 100, 122price priority, 57price-quantity quotes, 43prices. See under specific pricesprice-setting agents, 42pricing transparency, 2primary markets, 46, 49primary security dealers, 1prime brokerage agreements, 40principate (Rome), 3

Index 197

priority rules, 58product-investment decisions, 41profitability, 45profit-maximizing quote, 43promissory notes, 67property price, 155proprietary trading desks, 92Prussia, 181n52public funds, 32public listing, 146Pulvino, Todd (professor), 33

quote depth, 148quote rule, 42quote-driven markets, 50, 53–4quotes, 46–7, 50–4, 62–3, 65

railroad crash (1929), 115random walk, 10, 66, 81–3, 120–1ratings, credit, 21, 67, 86, 139real estate, 39–40, 85, 143

subprime market, 20–1, 28–9, 31, 36, 152realized value, 147–8real-time feeds, 46–7real-time information, 54real-world arbitrage, 12, 92–3real-world frictions, 6, 35real-world markets, 8, 13, 39, 101–2real-world transactions, 121, 124receivables, 5, 27, 31recovery rate, 138, 173n57redemptions, 24–5, 33–5, 126, 139reduced-form credit model, 128–9, 133refinancing, 5, 20, 26, 117

of commercial papers, 157of debt, 134, 141of short-term debt, 36

regression model, 123–4, 127Regulation Alternative Trading System (ATS),

51, 62–3Regulation National Market System (NMS),

52–3, 64regulations, 7, 39, 50–1, 67–8, 160–1

Basel III, 70, 145, 155–60See also policies

regulators, 19–20, 116, 119, 141–2Basel III and, 70defaulted Lehman debt and, 23in Europe, 46information from, 128, 155VaR and, 147vulnerabilities and, 155

regulatory policies, 40, 71–3, 117–18, 142reinvestment risk, 126, 179n32relative asset pricing, 12–13repurchase (repo) agreements, 22, 25–7, 91, 153

capital and, 66–9credit exposure and, 33–4during crisis, 157short-term, 29–30

repurchase (repo) clearing banks, 40repurchase (repo) funding, 71–2repurchase (repo) haircuts, 167n20repurchase (repo) markets, 29–30, 70, 153Reserve Primart Fund, 20, 23–4residential mortgage obligations, 50residential real estate, 39–40, 85resiliency, 9, 24, 45, 60, 70Resolution Trust Corporation, 76retail deposits, 158, 160retail investors, 25, 46–7, 56, 92, 146return risk, 23, 148, 150returns, 7, 12, 61–2, 76–9, 136–7

arbitrage and, 92–3in CAPM, 102–4illiquidity and, 32, 34–5liquidity and, 36, 143, 152market, 15–17, 78, 101, 104–8in Roll measure, 81–3VaR and, 149–50See also expected returns

Reuters (real-time feed), 46–7, 54reverse purchase transaction, 91risk aversion, 28, 42, 56, 154risk limits, 151risk management, 3–5, 26, 119, 133, 144

cash flows and, 184n24liquidity and, 81, 151VaR and, 147–8yield spread and, 117See also liquidity risks

risk managers, 14, 133, 141, 147–8risk premium. See liquidity risk premiumrisk weight of balance sheets, 145risk-free financing, 11risk-free interest rates, 14–16, 79, 115, 127

annual, 11asset value and, 136in CAPM, 102CDS-bond basis and, 119in HX model, 138in Shleifer-Vishny model, 94

riskless securities, 7, 79, 87risks. See under specific types of riskrisk-sharing agents, 6–7Roll, Richard (economist), 81–3Roll measure, 81–3, 122, 124, 150

modified, 121–2rollover effects, 134–5, 141rollover risk, 139, 141–2Rome, 3Rule 2a-7 (SEC), 22–4, 26

198 Index

rules. See policies; regulationsRussian debt crisis (1998), 17, 92, 105, 151

savings, 6Schumpeter, Joseph (economist), 66search costs, 55–6, 86, 88–9search efficiency, 90search-and-bargaining model, 77, 85–8, 101,

108–13vs. Diamond coconut model, 174n15OTC market and, 177n9

search-based economy, 86–7secondary markets, 24, 26, 49–50, 116, 141

for corporate bonds, 34, 115, 134–5second-round spillover losses, 152secured credit lines, 67securities. See under specific securitiesSecurities and Exchange Act (1934), 146Securities and Exchange Commission (SEC),

22–6, 42, 51–2, 169n12ITS order routing system and, 64net capital rule, 67

securities haircuts, 67Securities Industry and Financial Markets

Association (SIFMA), 21, 118, 145securitization, 5, 20–2, 27securitized assets, 21, 31, 35, 48sell orders. See buy and sell orderssell side, 54, 56senior management, 147settlements, 15, 40Shakespeare, William, 3share prices, 135–6shareholder base, 25Sharpe, William (Nobel laureate), 16Shleifer, Andrei (economist), 12, 93Shleifer-Vishny model, 93–4, 108shocks, 9, 22, 97, 104, 152

to bond market, 127, 135capital and, 34, 37, 100–1crisis and, 31, 144exogenous, 94, 134liquidity, 20–1, 134–7, 139–41of public information, 65–6resiliency and, 45to supply and demand, 68, 70

short selling, 66, 102, 120short-term agency notes, 26short-term borrowing, 20short-term cash inflows, 37short-term credit markets, 27, 156short-term debt, 27–8, 36short-term financing, 26, 30short-term funding, 22, 27, 161short-term loans, 28short-term repurchase (repo) agreements, 29

short-term securities, 24, 125–6short-term uncollateralized loans, 67Shylock (character in Merchant of Venice), 3slow-moving capital, 68small business loans, 160smart order routing systems, 46solvency, 30, 133, 160–1

insolvency, 29, 156Southeast Asia, 92sovereign debt crisis, 117Spatt, Chester, 58specialists, 67–8, 72speculative-grade bonds, 125–6, 139–41speed

of arbitrage, 35of dissemination, 44, 47, 64of execution, 56, 119of return to equilibrium, 45of trading, 58, 65of transacting, 53, 55, 72–3

spillover losses, 152spot markets, 91spreads. See bid-ask spreads; yield spreadssquare-root model, 129–31stable funding, 158–9Stafford, Eric (professor), 183n20Standard and Poor’s (S&P) 500 equity index, 11,

76, 92, 127standardization, 3, 48–9Stigum’s Money Market (reference guide), 23stochastic liquidity model, 102, 108stochastic time horizon, 151stochastic trading costs, 101stock exchanges. See under specific stock

exchangesstock market crash (1929), 4stock markets, 9–10, 17, 76, 143stock prices, 12, 62, 121stocks, 46, 72, 76, 78, 92

in Amihud measure, 84as assets, 17, 40vs. fixed-income securities, 116liquid vs. illiquid, 17, 144on NYSE vs. NASDAQ, 83risky, 62

Stoll, Hans (professor), 61–2, 83stores of value, 4–5structural credit model, 128, 133structured credit products, 20, 28structured finance, 21–2, 47structured securities, 35, 50, 153student loans, 31subordinate liabilities, 67subprime market, 20–1, 28–9, 31, 36, 152subprime residential mortgage-backed

securities, 5

Index 199

Subrahmanyam, Marti (professor), 123–4supply and demand, 21–2, 42, 110, 130

aggregate, 5–6, 60–1in general equilibrium economy, 7–8, 75, 97shocks to, 68, 70

surplus, 110syndication market, 35systemic crisis, 31, 144, 161systemic risks, 14–17, 78, 155, 160

compensation for, 101–2of corporate bonds, 126–7short-term funding and, 22

systemically important financial institutions (SIFIs), 155

taxes, 25, 76, 87, 134teaser rates, 5technological advances, 40–1, 50–3, 63–6, 72–3ten-year government agency bonds, 115term repurchase agreements, 25, 29term structure of bonds, 125, 133“The Only Game in Town” (Bagehot), 9theoretical arbitrage, 10, 34, 91–2theory of general equilibrium, 6third-party investors, 93, 95Tice, Sheri (professor), 143–4tick size, 48, 60–1, 65, 143–4Tier 1 capital, 158, 160Tier 2 capital, 158, 160tightness, 9time decay, 137, 147time deposits, 25time priority, 57time value of money, 11, 13, 127time-to-maturity, 137–8timing risks, 91Tirole, Jean (Nobel laureate), 27title insurance, 40Toft, Klaus (professor), 134“Towards a Fully Automated Exchange”

(Black), 9–10trade deficit, 4trade prices, 39, 47, 85, 89

of bonds, 130, 132–3Trade Reporting and Compliance Engine

(TRACE), 48, 116, 170n26trade volume, 49, 54, 61, 66

in Amihud measure, 84in Demsetz model, 7liquidity and, 8, 169n12on NYSE, 53in regression model, 124

trade-by-trade prices, 121–2traders, informed vs. uninformed, 39, 43–5,

47–8, 84, 169n10trade-through rule, 52, 64, 169n15

trading costs, 8–9, 36, 49, 75in asset pricing, 78in CAPM, 101, 103in HX model, 139in Roll measure, 83in search-and-bargaining model, 89

trading floor, 40trading protocols, 50trading queue, 60trading ratio, 71transacting, speed of, 53, 55, 72–3transaction costs, 7, 59–60, 75–80, 126, 135–7

bond value and, 139in CAPM, 102–3in dealer markets, 85liquidity as, 75, 107–8in Merton model, 133optimism on, 144in Roll’s measure, 83VaR and, 149

transaction flow, 45transaction prices, 8, 75, 81–5, 121–2

vs. fundamental values, 11information on, 50

transaction reports, 48transaction taxes, 78transaction volume, 84transparency, 2, 17, 46–9, 145–6Treasury auctions, 66Treasury bills, 79, 80Treasury bonds, 66, 91, 115, 160

yields of, 76, 118, 125Treasury debt, 118Treasury securities, 25–6tri-party repurchase (repo) and clearing

agreements, 40true benefit vs. true cost, 13true value of security, 39, 43–4

UBS PIN (ATS), 63uncertainty

ABCP and, 28during crisis, 20of default, 129, 134discount factors and, 14of future interest rates, 127of future prices, 17in liquid markets, 8–9of liquidity, 81, 101–2in liquidity shortages, 4of market value, 133MMFs and, 22vs. money, 39in order flow, 42risk management of, 5value of assets during, 44

200 Index

uncertainty—ContinuedYahoo Finance and, 46

underwriters, 146–7unsecured rates, 32US households

mortgages of, 5US Treasury, 70–1used cars, 44utility function, 13

valuein antiquity, 2–3continuation, of firms, 26discounted future, 80, 111–12discounted present, 127equity, 134–6stores of, 4–5time, of money, 11, 13, 127true, of security, 39, 43–4See also fundamental value;

market valuevalue-at-risk (VaR), 147–51Vanguard Group, 5variance swaps, 48Vietnam War, 4Vishny, Robert (economist), 12, 93volatility, 15, 62, 64, 149, 151

of assets, 58, 136, 139, 147–8volume of trade. See trade volume

volume-weighted average prices, 63voluntary disclosure, 146

walk, random, 10, 66, 81–3, 120–1Wall Street, 4Walras, Leon (economist), 7–8Walrasian auction, 7–8, 39, 41–2, 60–1Wang, Jiang (professor), 122–3, 127weekly liquid assets, 25–6Wei, Jason (professor), 125–6white noise, 82wholesale debt markets, 20wholesale deposits, 158, 160wholesale funding. See repurchase (repo)

agreementsWorld War II, 106

Xiong, Wei (professor), 134–5, 139

Yahoo Finance, 46yield, 23, 28, 70, 120, 146

of bonds, 68–9, 76, 91, 132–3search for, 117

yield spreads, 36, 115, 117–19, 139–41of corporate bonds, 34, 68, 119, 122–6

yield-to-maturity, 138

zero-coupon bonds, 93–4, 127, 130, 133zero-return measure, 124, 179n24

market liquidity risk

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