Market Microstructure in Practice

Contents
Foreword by
	Robert Almgren
	Bertrand Patillet
	Philippe Guillot
	Albert J. Menkveld
Preface by
	Preface of the editors to the second edition
	Charles-Albert Lehalle
	Sophie Laruelle
About the Editors
About the Contributors
Introduction
	Liquidity in Question
	Microstructure from a Regulatory Standpoint
		Future regulatory updates
		Influence of regulation on other asset classes
		Trading derivatives
	A Recent Appetite of Regulators and Policy-Makers for Electronic Markets
	Worldwide Consolidation: An Electronic Trading Global Timeline
	Changes in the Microstructure
	Defining Best Execution
	Redefining the Roles: An Agent is Now Both Liquidity Taker and Liquidity Provider Simultaneously
		Towards a paradigm shift: A blurring of roles
	Impact on Liquidity
1. Monitoring the Fragmentation at Any Scale
	1.1. Fluctuations of Market Shares: A First Look at Liquidity
		1.1.1. The market share: A not so obvious liquidity metric
		1.1.2. Phase 1: First attempts of fragmentation
		1.1.3. Phase 2: Convergence towards a European offer
		1.1.4. Phase 3: Apparition of broker crossing networks and dark pools
	1.2. SOR (Smart Order Routing), A Structural Component of European Price Formation Process
		1.2.1. How to route orders in a fragmented market?
		1.2.2. Fragmentation is a consequence of primary markets’ variance
	1.3. Still Looking for the Optimal Tick Size
		1.3.1. Why does tick size matter?
		1.3.2. How tick size affects market quality
		1.3.3. How can tick size be used by trading venue to earn market share?
		1.3.4. How does tick size change the profitability of the various participants in the market?
		1.3.5. The value of a quote
	1.4. Can We See in the Dark?
		1.4.1. Mechanism of dark liquidity pools
		1.4.2. In-depth analysis of dark liquidity
2. Understanding the Stakes and the Roots of Fragmentation
	2.1. From Intraday Market Share to Volume Curves: Some Stationarity Issues
		2.1.1. Inventory-driven investors need fixing auctions
		2.1.2. Timing is money: Investors’ optimal trading rate
		2.1.3. Fragmentation and the evolution of intraday volume patterns
	2.2. The Four Main Liquidity Variables: Traded Volumes, Bid–Ask Spread, Volatility and Quoted Quantities
	2.3. Does More Liquidity Guarantee a Better Market Share? A Little Story About the European Bid–Ask Spread
		2.3.1. The bid–ask spread and volatility move accordingly
		2.3.2. Bid–ask spread and market share are deeply linked
		2.3.3. Exchanges need to show volatility-resistance
	2.4. The Agenda of High Frequency Traders: How Do They Extend their Universe?
		2.4.1. Metrics for the balance in liquidity among indexes
		2.4.2. A history of coverage
		2.4.3. High-frequency traders do not impact all investors equally
	2.5. The Link Between Fragmentation and Systemic Risk
		2.5.1. The Spanish experiment
		2.5.2. The Flash Crash (May 6, 2010) in NY: How far are we from systemic risk?
		2.5.3. From Systemic Risk To Circuit Breakers
	2.6. Beyond Equity Markets
3. Optimal Organizations for Optimal Trading
	3.1. Organizing a Trading Structure to Answer a Fragmented Landscape
		3.1.1. Main inputs of trading tools
		3.1.2. Components of trading algorithms
		3.1.3. Main outputs of an automated trading system
	3.2. Market Impact Measurements: Understanding the Price Formation Process from the Viewpoint of One Investor
		3.2.1. Market impact over the trading period
		3.2.2. Market impact on a longer horizon: Price anticipation and permanent market impact
	3.3. The Price Formation Process and Orderbooks Dynamics
		3.3.1. Information reaching orderbooks
		3.3.2. Understanding via conditioning
		3.3.3. Conclusion on orderbook dynamics
	3.4. Optimal Trading Methods
		3.4.1. Algorithmic trading: Adapting trading style to investors’ needs
		3.4.2. Liquidity-seeking algorithms are no longer nice to have
		3.4.3. Conclusion on optimal trading
Appendix A: Quantitative Appendix
	A.1. From Entropy to FEI (Fragmentation Efficiency Index)
	A.2. Information Seeking and Price Discovery
	A.3. A Simple Model Explaining the Natural Fragmentation of Market Microstructure
		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
		A.3.4. Simulations
		A.3.5. Qualitative analysis
	A.4. Kyle’s Model For Market Making
	A.5. A Toy Model of the Flash Crash
		A.5.1. A market depth-oriented model
		A.5.2. Impact of the Flash Crash on our model
	A.6. Harris Model: Underlying Continuous Spread Discretized by Tick
	A.7. Optimal Trade Scheduling
		A.7.1. The trading model
		A.7.2. Towards a mean–variance optimal trade scheduling
		A.7.3. A Simple Stochastic Control Framework
	A.8. Estimation of Proportion and its Confidence Intervals
		A.8.1. Application to the estimation of the market share of venues on an asset
		A.8.2. Aggregation or application to the market share on an index
		A.8.3. Comparison of the estimators
	A.9. Gini Coefficient and Kolmogorov–Smirnov Test
		A.9.1. Gini coefficient
		A.9.2. Kolmogorov–Smirnov test
		A.9.3. Practical implementation
	A.10. Simple Linear Regression Model
		A.10.1. Model presentation
		A.10.2. Application to relation between spread and volatility
	A.11. Time Series and Seasonalities
		A.11.1. Introduction to time series
		A.11.2. Example of volume model
	A.12. Clusters of Liquidity
		A.12.1. Introduction to point processes
		A.12.2. One-dimensional Hawkes processes
		A.12.3. The propagator model
	A.13. Signature Plot and Epps Effect
		A.13.1. Volatility and signature plot
		A.13.2. Correlation and Epps effect
	A.14. Averaging Effect
		A.14.1. Mean vs. path
		A.14.2. Regression of average quantities vs. mean of the regressions
Appendix B: Glossary
Bibliography
Index