Financial Markets and Trading E-Book

Anatoly B. Schmidt

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Finance Editions
• Sprache: Englisch

Seitenzahl: 300

Veröffentlichungsjahr: 2011

Beschreibung

An informative guide to market microstructure and trading strategies

Over the last decade, the financial landscape has undergone a significant transformation, shaped by the forces of technology, globalization, and market innovations to name a few. In order to operate effectively in today's markets, you need more than just the motivation to succeed, you need a firm understanding of how modern financial markets work and what professional trading is really about. Dr. Anatoly Schmidt, who has worked in the financial industry since 1997, and teaches in the Financial Engineering program of Stevens Institute of Technology, puts these topics in perspective with his new book.

Divided into three comprehensive parts, this reliable resource offers a balance between the theoretical aspects of market microstructure and trading strategies that may be more relevant for practitioners. Along the way, it skillfully provides an informative overview of modern financial markets as well as an engaging assessment of the methods used in deriving and back-testing trading strategies.

• Details the modern financial markets for equities, foreign exchange, and fixed income
• Addresses the basics of market dynamics, including statistical distributions and volatility of returns
• Offers a summary of approaches used in technical analysis and statistical arbitrage as well as a more detailed description of trading performance criteria and back-testing strategies
• Includes two appendices that support the main material in the book

If you're unprepared to enter today's markets you will underperform. But with Financial Markets and Trading as your guide, you'll quickly discover what it takes to make it in this competitive field.

Leseprobe

Contents

Cover

Title Page

Copyright

Dedication

Preface

Acknowledgments

Part One: Market Microstructure

Chapter 1: Financial Markets: Traders, Orders, and Systems

Traders

Orders

The Bid/Ask Spread

Liquidity

Market Structures

Chapter 2: Modern Financial Markets

The U.S. Equity Markets

The U.S. Fixed Income Markets

High-Frequency Trading

Chapter 3: Inventory Models

Risk-Neutral Models

Models with Risk Aversion

Chapter 4: Market Microstructure: Information-Based Models

Kyle's Model

Glosten-Milgrom Model

Further Developments

Chapter 5: Models of the Limit-Order Markets

The CMSW Model

The Parlour Model

The Foucault Model

New Developments

Chapter 6: Empirical Market Microstructure

Roll's Model

The Glosten-Harris Model

Structural Models

Recent Empirical Findings

Part Two: Market Dynamics

Chapter 7: Statistical Distributions and Dynamics of Returns

Prices and Returns

The Efficient Market Hypothesis

Random Walk and Predictability of Returns

Recent Empirical Findings

Fractals in Finance

Chapter 8: Volatility

Basic Notions

Conditional Heteroskedasticity

Realized Volatility

Market Risk Measurement

Chapter 9: Agent-Based Modeling of Financial Markets

Adaptive Equilibrium Models

Non-Equilibrium Price Models

The Observable-Variables Model

Modeling Efficiency of Technical Trading

Modeling the Birth of a Two-Sided Market

Part Three: Trading Strategies

Chapter 10: Technical Trading Strategies

Trend Strategies

Momentum and Oscillator Strategies

Complex Geometric Patterns

Chapter 11: Arbitrage Trading Strategies

Hedging Strategies

Pair Trading

Arbitrage Risks

Chapter 12: Back-Testing of Trading Strategies

Performance Measures

Resampling Techniques

Comparing Trading Strategies

Chapter 13: Execution Strategies

Benchmark-Driven Schedules

Cost-Driven Schedules

The Taker's Dilemma

Appendix A: Probability Distributions

Basic Notions

Frequently Used Distributions

Stable Distributions and Scale Invariance

Appendix B: Elements of Time Series Analysis

The Autoregressive Model

The Moving Average Model

The ARMA Model

Trends and Seasonality

Multivariate Time Series

Notes

References

About the Author

Index

Copyright © 2011 by Anatoly B. Schmidt. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Schmidt, Anatoly B.

Financial markets and trading: an introduction to market microstructure and trading strategies / Anatoly B. Schmidt.

p. cm.—(Wiley finance; 637)

Includes bibliographical references and index.

ISBN 978-0-470-92412-9 (cloth); ISBN 978-1-118-09363-4 (ebk);

ISBN 978-1-118-09364-1 (ebk); ISBN 978-1-118-09365-8 (ebk)

1. Fixed-income securities. 2. Stock exchanges. 3. Microfinance. I. Title.

HG4650.S36 2011

332.64—dc22

2011008890

Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe, Australia, and Asia, Wiley is globally committed to developing and marketing print and electronic products and services for our customers' professional and personal knowledge and understanding.

The Wiley Finance series contains books written specifically for finance and investment professionals as well as sophisticated individual investors and their financial advisors. Book topics range from portfolio management to e-commerce, risk management, financial engineering, valuation, and financial instrument analysis, as well as much more.

For a list of available titles, visit our web site at www.WileyFinance.com.

Preface

The idea of writing this book came to me as a result of conversations with participants of meetings on quantitative finance and algorithmic trading, and with several generations of students doing internships in my group. I realized that there was a need for a single book that describes how modern financial markets work and what professional trading is about—a book devoted to the market microstructure and trading strategies.

The market microstructure theory has been an established field in finance. It has been thoroughly described in the graduate-level courses by O'Hara (1995), Hasbrouck (2007), and de Jong & Rindi (2009). Also, Harris (2002) has offered a detailed account on financial markets for practitioners. In the last decade, the landscape in this field has dramatically changed due to revolutionary changes in trading technology and the proliferation of electronic order-driven markets. The first goal of this book is to offer an overview of modern financial markets and the theoretical concepts of the market microstructure.

Trading is a process closely interwoven with the market microstructure. Indeed, in O'Hara's (1995) pioneering monograph, the market microstructure is defined in the following way: “While much of economics abstracts from the mechanics of trading, microstructure theory focuses on how specific trading mechanisms affect the price formation process.” According to Harris (2002), market microstructure is “a branch of financial economics that investigates trading and the organization of markets.” Also, de Jong & Rindi (2009) relate the market microstructure to the “process of financial price formation” and emphasize the importance of the market organization. Hence, while trading is widely discussed in the academic literature on the market microstructure, it is perceived primarily as a process of price formation. Yet, trading means much more for those who have ever traded for a living, for investing, or just for fun. The subject of trading strategies as a knowledge domain can be defined as studies of decision making on what, when, and how to trade. Much of its contents has been contributed by practitioners and may contain some subjective elements. Trading strategies have also received notable attention in academy, which has produced important methodological findings. Most of these results are scattered in periodical literature. The second goal of this book is to provide an overview of the main concepts and methods used in deriving and back-testing trading strategies.

This book is for any reader who is interested in the theoretical aspects of the market microstructure and trading. It can be used by students of undergraduate finance programs and may also be useful for masters-level courses in financial engineering and mathematical finance. I have tried to offer a balance between the theoretical aspects of the market microstructure and trading strategies that may be more relevant for practitioners. I have also included in the Appendix the basic elements of time series analysis and probability distributions, which are used in the presentation of the main material.

The book is organized into three parts:

Part I (Chapters 1 to 6) is an overview of modern financial markets for equities, FX, and fixed income. I start by introducing various types of traders, orders, and market structures, and then present the major market microstructure models. Finally, I describe some important empirical properties of modern equity and FX markets.

Part II (Chapters 7 to 9) addresses the basics of market dynamics, including statistical distributions, dynamics, and volatility of returns. I discuss the efficient market hypothesis and possible predictability of returns. I also introduce the concept of agent-based modeling of financial markets.

Part III (Chapters 10 to 13) is devoted to trading. It offers a summary of the concepts used in technical analysis and statistical arbitrage as well as a more detailed description of trading performance criteria and back-testing strategies. Finally, I discuss the ideas used in optimal order execution, such as optimal order slicing and the taker's dilemma.

Specifically, the book is structured as follows:

Chapter 1 gives a general description of financial markets. I describe the different types of traders and orders. Then, I introduce different market structures including quote- and order-driven markets, and continuous and call auctions.

Chapter 2 provides an overview of modern U.S. and European equity markets including major exchanges and alternative trading systems. I also introduce institutional FX and U.S. fixed income market structures. Finally, I go over the popular and somewhat controversial (in 2010) topic of high-frequency trading.

Chapters 3 through 5 are devoted to the main market microstructure models. In particular, in Chapter 3, I describe the inventory models including the risk-neutral models—Garman's (1976) model and Amihud-Mendelson (1980) model—and the Stoll's (1978) model with risk aversion. I introduce the informational models—the Kyle's (1985) model and the Glosten-Milgrom (1985) model—and their extensions in Chapter 4. Both inventory and informational models address the dealers markets. I review several models for limit-order markets—the Cohen-Maier-Schwartz-Whitcomb (1981) model, the Foucault (1999) model, the Parlour (1999) model, and their extensions—in Chapter 5.

Chapter 6 focuses on empirical market microstructure. First, I describe the Roll's (1984) model, the Glosten-Harris (1998) model, and the Hasbrouck's (1991, 2007) structural models, which are often used for interpreting empirical data. Then, I review intraday trading patterns, the specifics of order flows, and market impacts in equity markets and FX markets.

In Chapter 7, I provide an overview of statistical distributions and dynamics of returns. I address the problem of return predictability by reviewing the efficient market hypothesis and various types of the random walk. Then, I describe recent empirical data on statistical distributions of returns. Finally, I outline the concept of fractals and its applications in finance.

In Chapter 8, I focus on the volatility of returns. In particular, I provide an overview of various conditional heteroskedasticity models. Then, I describe current approaches to estimating the realized volatility. Finally, I outline the methods for measuring market risk.

In Chapter 9, I introduce the concepts of agent-based modeling of financial markets. I describe various trading patterns in terms of agent behavior and give an overview of two major families of agent-based models: (1) adaptive equilibrium models and (2) non-equilibrium price models.

Basic technical trading strategies are described in Chapter 10. I discuss the main concepts in chart trading, including trend-, momentum-, and oscillator-based trading strategies. I further introduce the head-and-shoulder pattern as an example of the complex geometric patterns that have gained popularity in technical trading.

Chapter 11 is devoted to arbitrage strategies. First, I give an overview of the main types of hedging strategies. Then, I focus on pair trading, which has a straightforward formulation in terms of the econometric concept of cointegration. Discussion of arbitrage risks concludes this chapter.

Back-testing of trading strategies is addressed in Chapter 12. First, I list the key performance criteria of trading strategies. Then, I provide an overview of the major resampling techniques (bootstrap and MCMC). I also introduce the random entry protocol that can be used for resampling coupled time series. Finally, I focus on the protocols for comparing trading strategies: White's (2000) bootstrap reality check and its extensions.

Chapter 13 is devoted to order execution strategies. First, I describe the benchmark-driven execution schedules (VWAP, TWAP, and other). Then, I focus on cost-driven execution schedules including the risk-neutral and risk-averse strategies. Finally, I describe the problem of choosing the order type (taker's dilemma).

There are two appendixes at the back of the book. Appendix A provides reference material on basic statistical notions and statistical distributions that are frequently used in finance. Appendix B describes the main concepts of time series analysis: autoregressive and moving average models, trends and seasonality, and multivariate models (vector autoregressive models).

The topics covered in this book are described using multiple sources. Though I made an effort to indicate the authors of new ideas in the field, most references are provided for further reading rather than for comprehensive chronological review. My choice of references to technical trading strategies and to time series analysis, which are extensively covered in the literature, is inevitably personal.

The following notations are used in the book interchangeably: X(tk) X(t) Xt, X(tk−1) X(t−1) Xt−1. E[X] is used to denote the expectation of the variable X. The conditional probability of event Y given event X is denoted with Pr(Y|X). Variables in the bold format refer to matrices and vectors.

The views expressed in this book are mine and may not coincide with the views of my former and current employers. I would greatly appreciate readers' comments, which can be sent to me at [email protected].

Anatoly B. Schmidt

Acknowledgments

Writing this book was my personal affair, about which few people knew. Craig Holder encouraged me to work on this project. Bill Falloon, Wiley's editor, took a risk by accepting it for publication.

I am grateful to members of the academic community for sharing with me their expertise. My special thanks go to Peter Hansen, Blake LeBaron, and Bruce Mizrach. Needless to say, all possible drawbacks of this book remain my sole responsibility.

Alas, my father Boris passed away before he could have seen this book. If I am able to crunch numbers, this comes from my dad. Boris did not have an opportunity to exploit his gift for math: He became an orphan while fleeing from Nazi-occupied Latvia to Russia and started working at the age of 16. My mother Ida taught literature for more than 40 years. I learned from her how to spend nights at my desk.

I am grateful to my wife Sofia and my children Mark and Sabina for their love and patience. I also constantly feel that they need me—and that's what helps me keep the pace.

Alec Schmidt

Part One

Market Microstructure

Chapter 1

Financial Markets: Traders, Orders, and Systems

This chapter describes a big picture of financial markets: who the traders are, what types of orders can be submitted, how these orders are processed, how prices are formed, and how markets are organized.

Traders

Let us start with the people who trade. They are called (well, you guessed it) traders. Those who trade for their own money (or their employer's money) are proprietary traders. Their ultimate goal is to make profits by buying low and selling high, whether it is long-term investment or day trading. Other traders execute orders for their clients. They are called brokers or agency traders. To denote the institutional character of a broker, the term brokerage (firm) is also used. For brokers, profits from trading may not be important since they receive commissions for trading and other services from their clients. Typical brokerage services include matching the clients' buy and sell orders, connecting to markets, clearing and settlement, providing market data and research, and offering credit. Most of the listed services are self-explanatory, but clearing and settlement may need some elucidation. Settlement is delivery of traded assets to the trading counterparts (buyers and sellers). The trading process (sometimes called the transaction) does not occur instantly. For example, settlement in the spot foreign exchange for most currencies takes two business days. Clearing denotes all brokerage actions that ensure settlement according to the market rules. These include reporting, credit management, tax handling, and so on.

The institutions that trade for investing and asset management (pension funds, mutual funds, money managers, etc.) are called the buy-side. The sell-side provides trading services to the buy-side. Besides brokers, the sell-side includes dealers who buy and sell securities upon their clients' requests. In contrast to brokers, dealers trade for their own accounts. Hence, they have a business model of proprietary traders. Namely, dealers make profits by selling an asset at a price higher than the price at which they simultaneously buy the same asset.1 Providing an option to buy and sell an asset simultaneously, dealers are market makers who supply liquidity to the market (see more about liquidity below). Traders who trade with market makers are sometimes called takers. Many sell-side firms have brokerage services and are called broker-dealers.

Harris (2002) provides a detailed taxonomy of various trader types. Here I offer a somewhat simplified classification. There are two major groups: (1) profit-motivated traders and (2) utilitarian traders. Profit-motivated traders trade only when they rationally expect to profit from trading. Utilitarian traders trade if they expect some additional benefits besides (and sometimes even instead of) profits. Investors who trade for managing their cash flows are the typical example of utilitarian traders. Indeed, when an investor sells (part of) his equity portfolio to get cash for buying a house, or invests part of his income on a periodic schedule, his trades may be not optimal in the eyes of pure profit-motivated traders. Hedgers are another type of utilitarian traders. The goal of hedging is to reduce the risk of owning a risky asset. A typical example is buying put options for hedging equities. Put options allow the investor to sell stocks at a fixed price.2 The immediate expenses of buying options may be perceived as a loss. Yet these expenses can protect the investor from much higher losses in case of falling stock price. In the economic literature, utilitarian traders are often called liquidity traders to emphasize that they consume the liquidity that is provided by market makers.

Profit-motivated traders can be partitioned into informed traders, technical traders, and dealers.3 Informed traders base their trading decisions on information on the asset fundamental value. They buy an asset if they believe it is underpriced in respect to the fundamental value and sell if the asset is overpriced. Since buying/selling pressure causes prices to increase/decrease, informed traders move the asset price toward its fundamental value. Traders who conduct thorough fundamental analysis of the asset values, such as the company's profits, cash flow, and so on, are called value investors (Graham & Dodd 2006). Note that fundamental values do not always tell the entire story. New information that comes in the form of unexpected news (e.g., discovery of a new technology, introducing a new product by a competitor, CEO resignation, or a serious accident, etc.) can abruptly challenge the asset price expectations. Also, estimates of the fundamental value of an asset may vary across different markets. Traders who explore these differences are called arbitrageurs (see Chapter 11).

Technical traders believe that the information necessary for trading decisions is incorporated into price dynamics. Namely, technical traders use multiple patterns described for historical market data for forecasting future price direction (see Chapter 10).

As it was indicated above, dealers (market makers) supply liquidity to other traders. In some markets, traders who are registered as dealers receive various privileges, such as exclusive handling of particular securities, lower market access fees, and so on. In return, dealers are required to always provide at least a minimum number of securities (in which they make the market) for buying and selling. Dealers make profits from the difference between the selling price and buying price that they establish. This implies that there are takers in the market who are willing to buy a security at a price higher than the price at which they can immediately sell this security. It seems like easy money providing that the price does not change and there are equal flows of buying and selling orders. Obviously, there is always a risk that dealers have to replenish their inventory by buying security at a price higher than the price they sold this security in the near past. This may be caused by a sudden spike in demand caused by either informed or liquidity traders. Similarly, dealers' loss may ensue when takers exert selling pressure. We shall return to the dealers' costs in Chapters 3 and 4.

Orders

When traders decide to make a trade, they submit orders to their brokers. Order specifies the trading instrument, its quantity (size), market side (buy or sell), price, and some other conditions (if any) that must be met for conducting a trade. When orders find their counterparts in the markets, a transaction occurs and it is said that orders are matched (or filled). Orders are submitted upon some market conditions. If these conditions change before an order is matched, the trader can cancel and possibly resubmit an order with other properties. Here the latency problem may become important. Traders do not receive confirmations of their trades instantly. If a trader attempts to cancel the order while it is being transacted, the cancellation fails.

There are two major order types: market orders and limit orders. Price is not specified for market orders and these orders are executed at the best price available at the order arrival time (i.e., a bid/offer order is filled at the current best offer/bid price). Limit buy and sell orders are quoted in the market with their bid and ask (or offer) prices, respectively. Prices of limit orders are sometimes called reservation prices. The highest bid and lowest ask (offer) currently present in the market are called best bid and best ask (offer), respectively. The difference between the best ask and the best bid is the bid/ask spread (see the next section). The bid/ask bounce of transaction prices is caused by trades randomly initiated by buy and sell orders. As a result, sequential transaction prices fluctuate between the best ask and best bid prices. It is said that any price within the spread is inside the market. The half-sum of the best bid and best ask is called mid-point price (or just mid-price).

Limit orders specify the worst price (highest offer or lowest bid) at which traders agree to trade. If a better price is available for matching the limit order at the time of its arrival, the transaction is done at the better price. Limit orders are not guaranteed to be executed. For example, a limit buy order is placed below the best bid but the price does not fall that low. Limit orders that are not immediately filled are stored in the limit order book (LOB) until they are matched or cancelled (see more about the LOB below). It is important to remember that the aggregated order size at any price in the LOB is finite. Hence, a large market order may wipe out the entire LOB inventory at best price and get filled not at a single price but within some price range. As a result, the best price worsens—at least temporarily. It is said that large orders have market impact. Some markets do not permit market orders and limit orders are the only option.

In some markets, all limit orders are automatically cancelled at the end of trading day. To prevent such a cancellation, an option known as good-till-cancelled may be available. Usually, such an option has a limited duration (e.g., one month).

For a trader, the choice between a limit order and market order (when the latter order is permitted) may be non-trivial. For example, a trader assuming a long position can submit a market buy order and fill an order at current best offer price. In other words, the trader becomes a taker. Another option is to submit a limit order at a current best bid (or even at a lower) price—that is, become a maker. It is said that takers pay the spread, which is the price for immediacy. Indeed, there is a risk that the price will move in an adverse direction and the maker order will not be executed within the acceptable time horizon. We shall return to this problem in Chapter 13. As I have indicated above, taker order is always filled at the best available price. Namely, a bid/ask submitted with a price higher/lower than the best ask/bid is still filled at the current best ask/bid. In general, limit orders submitted across the market are called marketable limit orders. Why would anyone submit such an order? This may happen if a trader wants to make a sure shot with at least partial filling but not to pay beyond some limit price.

Some markets permit hidden limit orders. These orders have a lower priority in respect to visible orders at the same price but higher priority than the limit orders with worse price. Sometimes orders can be partially hidden. In the latter case, when the visible order part is filled, it is replenished with the hidden amount and the order position in the LOB is preserved.

Cancel and replace limit orders allow traders to change the order size without losing the order position in the LOB.

Limit orders can be pegged in some markets. There are three ways to define pegged order. The first definition involves primary (market)—peg to the best price on the same (opposite) side of the market. Also, orders can be pegged to the bid/ask mid-price. The price of unfilled (e.g., due to latency) pegged orders moves along with their peg.

Some markets have an option to submit market-on-open and market-on-close orders. These orders are submitted in advance for executing at a new market opening and closing, respectively.

Stop orders can be treated as limit orders since they, too, specify the execution price. However, price has a different role in stop orders: It constrains possible loss rather than yields the realized profit. Indeed, a trader sells an instrument using a limit order at a higher price for locking in the profit after buying an instrument at a lower price. On the contrary, the sell stop order is filled when price falls to (or below) the order price. Hence, traders submit stop orders for mitigating the risk of possible adverse price moves.

Some other instructions may be provided with orders. Fill-or-kill orders are filled at their arrival in the market. Any portion of such an order that cannot be immediately filled is cancelled. Another constraint is used in the all-or-none orders: These orders can be filled completely, or not at all.

So far, selling implies that the trader owns the selling asset (i.e., has a long position in it). Short selling, or acquiring a short position, means that the trader borrows an asset from his broker and sells it. This makes sense if there is expectation that the asset price will fall. Then the trader buys the same asset (presumably) at a lower price for returning it to the broker and pockets the difference. Two special order types are used to implement this strategy: sell short and buy to cover. Note that the so-called uptick rule forbids short selling unless a short order is submitted either at a price above the last traded price, or at the last traded price if that price was higher than the price in the previous trade. In the United States, the uptick rule was in effect for many years until it was canceled in 2007. However, discussions on the necessity of this rule resumed in 2009 in the context of introducing a stricter regulation of financial markets.

The Bid/Ask Spread

The size of the bid/ask spread is an important object of the microstructure theory, which shall be addressed in future chapters. Here is a list of common definitions and components of the spread (de Jong & Rindi 2009).

The quoted spread between ask At and bid Bt that is averaged over T periods equals

(1.1)

In terms of the asset fundamental price, , the averaged spread is

(1.2)

where qt is 1 for buy orders and −1 for sell orders. Since the value of is not observable, the effective spread in terms of mid-price Mt = 0.5(At + Bt) is usually used:

(1.3)

Sometimes the realized spread is applied in post-trade analysis:

(1.4)

It was already indicated that the bid/ask spread from the point of the view of a taker is the price for immediacy of trading. Now, let's examine the main components of the bid/ask spread, which are determined by dealers (makers).

First, the spread incorporates the dealers' operational costs, such as trading system development and maintenance, clearing and settlement, and so on. Indeed, if dealers are not compensated for their expenses, there is no rationale for them to stay in this business.

Dealer's inventory costs, too, contribute into the bid/ask spread. Since dealers must satisfy order flows on both sides of the market, they maintain inventories of risky (and sometimes undesirable) instruments. The inventory microstructure models will be discussed in Chapter 3. Glosten & Harris (1988) combine the operational and inventory costs into a single transitory component, since their effect on security price dynamics is unrelated to the security value. Another spread component reflects the dealer's risk of trading with counterparts who have superior information about true security value. Informed traders trade at one side of the market and may profit from trading with dealers. Hence, dealers must recover their potential losses by widening the spread. Not surprisingly, dealers pass these losses to uninformed traders.4 This component of the bid/ask spread is called the adverse-selection component since dealers confront one-sided selection of their order flow. The adverse selection will be discussed in Chapter 4.

Liquidity

Liquidity is a notion that is widely used in finance, yet it has no strict definition and in fact may have different meanings. Generally, the term liquid asset implies that it can be quickly and cheaply sold for cash. Hence, cash itself (i.e., money) is the ideally liquid asset. Real estate and antique on the other hand are not very liquid.

In the context of trading, liquidity characterizes the ability to trade an instrument without notable change of its price. A popular saying defines liquidity as the market's breadth, depth, and resiliency. First, this implies that the buying price and selling price of a liquid instrument are close, that is, the bid/ask spread is small. In a deep market, there are many orders from multiple makers, so that order cancellations and transactions do not affect notably the total order inventory available for trading. Finally, market resiliency means that if some liquidity loss does occur, it is quickly replenished by market makers. In other words, market impact has a temporary character. As we shall see in Chapter 13, analysis of market impact dynamics is a rather complex problem.

Various liquidity measures are used in different markets. For example, Xetra's (the European electronic trading system) liquidity measure corresponds to the relative market impact costs for the so-called round trip (simultaneous buying and selling of a position) for a given order size (Gomber & Schweickert 2002). Barclays Capital derives the FX liquidity index using the notional amounts traded for a fixed set of FX spreads and aggregated using a weighting by currency pair (quoted by Bank of England 2009). Sometimes inverse liquidity (illiquidity), based on the price impact caused by trading volume, is used (Amihud 2002):

(1.5)

In (1.5), rk and Vk are the return and trading volume for time interval k. The notion of liquidity is rooted in the Kyle's model (1985), which will be discussed in Chapter 4.

Market Structures

Markets differ in their organization and trading rules. Some markets that are highly organized and regulated by government agencies are called exchanges (or bourses). In the United States, the trading of stocks, bonds, and several other securities is regulated by the Securities and Exchanges Commission (SEC). However, trading of commodities (including spot, futures, and options) is regulated by another government agency, the Commodity Futures Trading Commission (CFTC).

Historically, exchanges were founded by their members (dealers, brokers) for trading among themselves. In our days, many exchanges have become incorporated. Still, in most cases only members can trade at exchanges. An alternative to exchanges is the over-the-counter (OTC) markets where dealers and brokers can trade directly.

Market structure is defined with specifics of execution systems and with the type of trading sessions (Harris 2002; de Jong & Rindi 2009). There are two major execution systems: order-driven markets and quote-driven markets. In terms of trading sessions, order-driven markets can be partitioned into continuous markets and call markets. Many order-driven markets are auctions in which trading rules ensure that trading occurs at the highest price a buyer is willing to pay and at the lowest price a seller is willing to sell at. The process of defining such a price is called price discovery (or market clearing).

Another form of order-driven markets is crossing networks. Price discovery is not implemented in crossing networks. Instead, prices used in matching are derived from other (primary) markets. Hence, the term derivative pricing rules is used.5 Orders submitted to crossing networks have no price and are prioritized according to their arrival time. The first advantage of crossing networks is that trading in these systems is (or at least is supposed to be) completely confidential. Another advantage is that trading there does not have an impact on price in the primary markets. Hence, crossing networks are attractive to those traders who trade orders of large size (blocks). On the other hand, trading in the dark may encounter significant order imbalance. It is usually calculated as a difference between aggregated demand and aggregated supply and is also called excess demand. As a result, the portion of filled orders (fill ratio) may be rather small. Another drawback of crossing networks is the imperfection of the derivative pricing that may be subject of manipulations. More specifics on different market structures will be detailed in the following sections.

Continuous Order-Driven Markets

In continuous markets, traders can submit their orders at any time while the market is open. Trading hours vary in different markets. For example, the New York Stock Exchange (NYSE) and NASDAQ are open on Monday through Friday; they start at 9:30 a.m. EST and close at 4:00 p.m. EST. On the other hand, the global FX spot market is open around the clock during the workdays and is closed only for a few hours on weekends (see more on FX markets in Chapter 2).

In order-driven markets, traders trade among themselves without intermediary market makers (dealers). In other words, every trader can become a market maker by placing a limit order. Those limit orders that are not immediately matched upon arrival are entered into the LOB according to the price-time priorities. Price priority has the primary precedence, which means that an order with a better (or more aggressive) price is placed before orders with worse prices.

Time priority means that a new order is placed behind the orders that have the same price and entered the market earlier. Matching of a new taker order with maker orders present in the LOB occurs upon the First In, First Out (FIFO) principle—that is, older maker orders are filled first. It is said that the first order with the best price is on top of the order book. Hence, the higher/lower the bid/offer order price is, the closer this order is to the top of the LOB.

In some markets, size precedence rule is used. Sometimes the largest order is executed in case of a parity of several orders, but sometimes the priority is given to the smallest order. Another option is pro rata allocation. Say the aggregated bid size exceeds the aggregated offer size. Then all bid orders are partially filled proportionally to their size.

Consider a few examples of matching in an order-driven market. Let the LOB have the following bids:6 B1 –[email protected] (best bid), B2 – [email protected], and the following offers: O1 – [email protected] (best offer), O2 – [email protected]. A market buy order of a size less or equal to 200 will be filled at the price P = 10.30. If the size of the market buy order equals 200, it completely matches O1 and the bid/ask spread increases from s = 10.30 − 10.25 = 0.05 to s = 10.35 − 10.25 = 0.1.