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

Transcription

1 Microstructure YJ? World Scientific NEW JERSEY LONDON SINGAPORE BEIJING SHANGHAI HONG KONG TAIPEI CHENNAI

2 Contents About the Editors Ab out the Contributors Foreword by Robert Almgren Bertrand Patillet Philippe Guillot Albert J. Menkveld Preface by Charles-Albert Lehalle Sophie Lamelle v vii xv xv xvi xvii xix xxi xxi xxiv Introduction 1 1. Monitoring the Fragmentation at Any Scale Fluctuations of Market Shares: A First Graph on Liquidity The market share: A not so obvious liquidity metric Phase 1: First attemptsof fragmentation Phase 2: Convergence towards a European offer Phase 3: Apparition of broker crossing networks and Dark Pools Smart Order Routing (SOR), A Structural Component of European Price Formation Process 56 ix

3 X Market Microstructure in Practice 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? How tick size affects market quality How can tick size be used by trading vertue 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 need to trade accordingly Fragmentation and the evolution of intraday volume patterns 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? 144

4 Contents xi 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 Volatility, cross-stock correlation, intraday, extraday The Flash Crash (May 6th, 2010) in NY: How far are we from systemic risk? Optimal Organisations for Optimal Trading Organising a Trading Structure to Answer to 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 Better understanding on what impacts the price Market impact over the trading period Market impact on a longer horizon: Different patterns for different Investment styles Dependence between Investment style and market impact on a monthly horizon Optimal Trading Methods Algorithmic trading: Adapting trading style to Investors' needs Liquidity seeking algorithms are no longer nice to have 210 Appendix A: Quantitative Appendix 221 A.l From Entropy to FEI (Fragmentation Efficiency Index) 221

5 Market Microstructure in Practice A.2 Information Seeking and Price Discovery 225 A.3 A Simple Model Explaining the Natural Fragmentation of Market Microstructure 229 A.3.1 A toy model of SOR dynamics A.3.2 A toy model of the impact of SOR activity on the market shares A.3.3 A coupled model of SOR-market shares dynamics 232 A.3.4 Simulations 232 A.3.5 Qualitative analysis 233 A.4 A Toy Model of the Flash Crash 235 A.4.1 A market depth-oriented model A.4.2 Impact of the Flash Crash on our model 237 A.5 Harris Model: Underlying Continuous Spread Discretized by Tick 240 Motivation and notations 240 Numerical application 243 A.6 Optimal Trade Scheduling 247 A.6.1 The trading model 248 A.6.2 Towards a mean-variance optimal trade scheduling 249 A.7 Estimation of Proportion and its Confidence Intervals 255 A.7.1 Application to the estimation of the market share of venues on an asset 256 A.7.2 Aggregation or application to the market share on an index 256 A.7.3 Comparison of the estimators A.8 Gini Coefficient and Kolmogorov-Smirnov Test A.8.1 Gini coefficient 259 A.8.2 Kolmogorov-Smirnov test 260 A.8.3 Practical implementation 261 A.9 Simple Linear Regression Model 264 A.9.1 Model presentation 265 A.9.2 Application to relation between spread and volatility 266

6 Contents xiii A.10 Time Series and Seasonalities 270 A.10.1 Introduction to time series 270 A.10.2 Example of volume model 274 A.ll Clusters of Liquidity 276 A.ll.l Introduction to point processes A.ll.2 One-dimensional Hawkes processes 279 A.12 Signatare Plot and Epps Effect 283 A.12.1 Volatility and signature plot A.12.2 Correlation and Epps effect A.13 Averaging Effect 286 A.13.1 Mean versus path 286 A.13.2 Regression of average quantities versus mean of the regressions Appendix B: Glossary 289 Bibliography 297 Index 303