? World Scientific NEW JERSEY. LONDON SINGAPORE BEIJING SHANGHAI HONG KONG TAIPEI CHENNAI

Transcription

1 " 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

2 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 Fluctuations of Market Shares: A First Look at Liquidity The market share: A not so obvious liquidity metric Phase 1: First attempts of fragmentation Phase 2: Convergence towards a European offer 50 xxi

3 xxii Market Microstructure in Practice Phase 3: Apparition of broker crossing networks and dark pools SOR (Smart Order Routing), A Structural Component of European Price Formation Process How to route orders in a fragmented market? Fragmentation is a consequence of primary markets' variance Still Looking for the Optimal Tick Size Why does tick size matter? Flow tick size affects market quality How can tick size be used by trading venue to earn market share? How does tick size change the profitability of the various participants in the market? The value of a quote Can We See in the Dark? Mechanism of dark liquidity pools In-depth analysis of dark liquidity Understanding the Stakes and the Roots of Fragmentation From Intraday Market Share to Volume Curves: Some Stationarity Issues Inventory-driven Investors need fixing auctions Timing is money: Investors' optimal trading rate Fragmentation and the evolution of intraday volume patterns The Four Main Liquidity Variables: Traded Volumes, Bid-Ask Spread, Volatility and Quoted Quantities 143

4 Contents xxiii 2.3 Does More Liquidity Guarantee a Better Market Share? A Little Story About the European Bid-Ask Spread The bid-ask spread and volatility move accordingly Bid-ask spread and market share are deeply linked Exchanges need to show volatility-resistance The Agenda of High Frequency Traders: How Do They Extend their Universe? Metrics for the balance in liquidity among indexes A history of coverage High-frequency traders do not impact all investors equally The Link Between Fragmentation and Systemic Risk The Spanish experiment The Flash Crash (May 6, 2010) in NY: How far are we from systemic risk? From Systemic Risk To Circuit Breakers Beyond Equity Markets Optimal Organizations for Optimal Trading Organizing a Trading Structure to Answer a Fragmented Landscape Main inputs of trading tools Components of trading algorithms Main Outputs of an automated trading system Market Impact Measurements: Understanding the Price Formation Process from the Viewpoint of One Investor Market impact over the trading period. 204

5 xxiv Market Microstructure in Practice Market impact on a longer horizon: Price anticipation and permanent market impact The Price Formation Process and Orderbooks Dynamics Information reaching orderbooks Understanding via conditioning Conclusion on orderbook dynamics Optimal Trading Methods Algorithmic trading: Adapting trading style to Investors' needs Liquidity-seeking algorithms are no longer nice to have 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

6 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

7 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