" u*' ' - Microstructure in Practice Second Edition Editors Charles-Albert Lehalle Capital Fund Management, France Sophie Lamelle Universite Paris-Est Creteil, France? World Scientific NEW JERSEY. LONDON SINGAPORE BEIJING SHANGHAI HONG KONG TAIPEI CHENNAI
Contents Foreword by Robert Almgren Bertrand Patillet Philippe Guillot Albert J. Menkveld Preface by Charles-Albert Lehalle Sophie Lamelle About the Editors About the Contributors v v vi vii ix xi xii xv xvii xix Introduction 1 1. Monitoring the Fragmentation at Any Scale 33 1.1 Fluctuations of Market Shares: A First Look at Liquidity 33 1.1.1 The market share: A not so obvious liquidity metric 33 1.1.2 Phase 1: First attempts of fragmentation 39 1.1.3 Phase 2: Convergence towards a European offer 50 xxi
xxii Market Microstructure in Practice 1.1.4 Phase 3: Apparition of broker crossing networks and dark pools 54 1.2 SOR (Smart Order Routing), A Structural Component of European Price Formation Process 62 1.2.1 How to route orders in a fragmented market? 62 1.2.2 Fragmentation is a consequence of primary markets' variance 71 1.3 Still Looking for the Optimal Tick Size 74 1.3.1 Why does tick size matter? 74 1.3.2 Flow tick size affects market quality 77 1.3.3 How can tick size be used by trading venue to earn market share? 91 1.3.4 How does tick size change the profitability of the various participants in the market? 97 1.3.5 The value of a quote 100 1.4 Can We See in the Dark? 102 1.4.1 Mechanism of dark liquidity pools... 102 1.4.2 In-depth analysis of dark liquidity... 105 2. Understanding the Stakes and the Roots of Fragmentation 117 2.1 From Intraday Market Share to Volume Curves: Some Stationarity Issues 117 2.1.1 Inventory-driven Investors need fixing auctions 119 2.1.2 Timing is money: Investors' optimal trading rate 129 2.1.3 Fragmentation and the evolution of intraday volume patterns 139 2.2 The Four Main Liquidity Variables: Traded Volumes, Bid-Ask Spread, Volatility and Quoted Quantities 143
Contents xxiii 2.3 Does More Liquidity Guarantee a Better Market Share? A Little Story About the European Bid-Ask Spread 148 2.3.1 The bid-ask spread and volatility move accordingly 150 2.3.2 Bid-ask spread and market share are deeply linked 153 2.3.3 Exchanges need to show volatility-resistance 156 2.4 The Agenda of High Frequency Traders: How Do They Extend their Universe? 158 2.4.1 Metrics for the balance in liquidity among indexes 159 2.4.2 A history of coverage 161 2.4.3 High-frequency traders do not impact all investors equally 163 2.5 The Link Between Fragmentation and Systemic Risk 169 2.5.1 The Spanish experiment 170 2.5.2 The Flash Crash (May 6, 2010) in NY: How far are we from systemic risk?... 177 2.5.3 From Systemic Risk To Circuit Breakers 187 2.6 Beyond Equity Markets 189 3. Optimal Organizations for Optimal Trading 193 3.1 Organizing a Trading Structure to Answer a Fragmented Landscape 193 3.1.1 Main inputs of trading tools 194 3.1.2 Components of trading algorithms... 197 3.1.3 Main Outputs of an automated trading system 198 3.2 Market Impact Measurements: Understanding the Price Formation Process from the Viewpoint of One Investor 203 3.2.1 Market impact over the trading period. 204
xxiv Market Microstructure in Practice 3.2.2 Market impact on a longer horizon: Price anticipation and permanent market impact 209 3.3 The Price Formation Process and Orderbooks Dynamics 215 3.3.1 Information reaching orderbooks... 217 3.3.2 Understanding via conditioning 219 3.3.3 Conclusion on orderbook dynamics 226 3.4 Optimal Trading Methods 227 3.4.1 Algorithmic trading: Adapting trading style to Investors' needs 227 3.4.2 Liquidity-seeking algorithms are no longer nice to have 233 3.4.3 Conclusion on optimal trading 244 Appendix A: Quantitative Appendix 247 A.l From Entropy to FEI (Fragmentation Efficiency Index) 247 A.2 Information Seeking and Price Discovery 250 A.3 A Simple Model Explaining the Natural Fragmentation of Market Microstructure 253 A.3.1 A toy model of SOR dynamics 255 A.3.2 A toy model of the impact of SOR activity on the market shares 256 A.3.3 A coupled model of SOR-market shares dynamics 257 A.3.4 Simulations 258 A.3.5 Qualitative analysis 259 A.4 Kyle's Model For Market Making 260 A.5 A Toy Model of the Flash Crash 261 A.5.1 A market depth-oriented model 262 A.5.2 Impact of the Flash Crash on our model 263 A.6 Harris Model: Underlying Continuous Spread Discretized by Tick 266
Contents xxv A.7 Optimal Trade Scheduling 273 A.7.1 The trading model 275 A.7.2 A.7.3 Towards a mean-variance optimal trade scheduling 276 A Simple Stochastic Control Framework 281 A.8 Estimation of Proportion and its Confidence Intervals 284 A.8.1 A.8.2 Application to the estimation of the market share of venues on an asset 286 Aggregation or application to the market share on an index 286 A.8.3 Comparison of the estimators 287 A.9 Gini Coefficient and Kolmogorov-Smirnov Test 288 A.9.1 Gini coefficient 288 A.9.2 Kolmogorov-Smirnov test 289 A.9.3 Practical implementation 291 A.10 Simple Linear Regression Model 292 A.10.1 Model presentation 293 A.10.2 Application to relation between spread and volatility 295 A.ll Time Series and Seasonalities 298 A.ll.l Introduction to time series 298 A.11.2 Example of volume model 302 A.12 Clusters of Liquidity 304 A.12.1 Introduction to point processes 305 A.12.2 One-dimensional Hawkes processes 308 A.12.3 The propagator model 311 A.13 Signature Plot and Epps Effect 316 A.13.1 Volatility and signature plot 316 A.13.2 Correlation and Epps effect 318
xxvi Market Microstructure in Practice A.14 Averaging Effect 318 A.14.1 Mean vs. path 319 A.14.2 Regression of average quantities vs. mean of the regressions 319 Appendix B: Glossary 323 Bibliography 331 Index 337