Title Details

FRÉDÉRIC ABERGEL graduated from École Normale Supérieure with a PhD in Mathematics. He started an academic career as a researcher with the CNRS. He spent ten years in the Mathematics department of the University of Orsay Paris XI and then switched to the capital markets industry and became a quantitative analyst. He has worked for trading floors in various financial institutions, mainly in the derivatives sector, developing pricing and hedging models. He now holds the BNP Paribas Chair of Quantitative Finance at École Centrale Paris. His research focuses on the study of empirical properties and mathematical models of market microstructure, high frequency data, algorithmic trading. 

JEAN-PHILIPPE BOUCHAUD graduated from the École Normale Supérieure in Paris, where he also obtained his PhD in physics. He was then appointed by the CNRS. After a year spent in the Cavendish Laboratory, he joined the Service de Physique de l’Etat Condensé, where he worked on the dynamics of glassy systems and on granular media. His work in finance includes extreme risk models, agent based simulations, market microstructure and price formation. He went on to found the company Science & Finance that merged with Capital Fund Management. He is now the President and Head of Research at CFM, and professor at École Polytechnique. He has published over 250 scientific papers and several books in physics and in finance. 

THIERRY FOUCAULT is Professor of Finance at HEC, Paris where he received his PhD in Finance. He is a research fellow of the Centre for Economic Policy. His research focuses on the determinants of financial markets liquidity and the industrial organization of the securities industry. His work has been published in top-tier scientific journals, including the Journal of Finance, the Journal of Financial Economics, and the Review of Financial Studies. He acts as co-editor of the Review of Finance and he is an Associate Editor of the Review of Asset Pricing Studies. For his research, he received awards from the Europlace Institute of Finance in 2005 and 2009, the annual research prize of the HEC Foundation in 2006 and 2009, and the Analysis Group award for the best paper on Financial Markets and Institutions presented at the 2009 Western Finance Association meetings. 

CHARLES-ALBERT LEHALLE is the Head of Quantitative Research at CA Cheuvreux and is an international expert in optimal trading. He published papers in international journals about the use of stochastic control and stochastic algorithms to optimise a trading flow with respect to flexible contraints. He also authored papers on post trade analysis, market impact estimates and modelling the dynamics of limit order books. He lectures at Paris 6 (El Karoui) Master of Finance (École Polytechnique, ESSEC, École Normale Supérieure) and MASEF/ENSAE, and gives master classes in the Certificate in Quantitative Finance in London. He holds a PhD in Applied Mathematics and his core fields are stochastic processes, information theory and nonlinear control. 

MATHIEU ROSENBAUM gained is PhD from University Paris-Est. He is now Professor at University Pierre et Marie Curie (Paris 6) and École Polytechnique and is a member of the CREST (Center of Research in Economics and Statistics). His research mainly focuses on statistical finance problems, such as market microstructure modeling or designing statistical procedures for high frequency data. He also has research collaborations with several financial institutions, in particular BNP Paribas.

Introduction

About the Editors

PART I ECONOMIC MICROSTRUCTURE THEORY

1 Algorithmic Trading: Issues and Preliminary Evidence
Thierry Foucault

1.1 Introduction

1.2 What is algorithmic trading?

1.2.1 Definition and typology

1.2.2 Scope and profitability

1.3 Market structure and algorithmic trading

1.4 Costs and benefits of algorithmic trading

1.4.1 Algorithmic trading reduces search costs

1.4.2 Algorithmic trading has an ambiguous effect on adverse selection costs

1.4.3 Algorithmic trading and price discovery

1.4.4 Welfare effects

1.4.5 Algorithmic trading as a source of risk

1.5 Empirical evidence

1.5.1 Algorithmic trading and market liquidity

1.5.2 Algorithmic trading and volatility

1.5.3 Algorithmic trading and price discovery

1.5.4 Algorithmic trading and market stability

1.6 Conclusions

Appendix

Acknowledgment

References

2 Order Choice and Information in Limit Order Markets 41
Ioanid Roᶊu

2.1 Introduction

2.2 Order choice with symmetric information

2.3 Order choice with asymmetric information

2.4 The information content of orders

2.5 Questions for future research

References

PART II HIGH FREQUENCY DATA MODELING

3 Some Recent Results on High Frequency Correlation
Nicolas Huth and Frédéric Abergel

3.1 Introduction

3.2 Data description

3.3 Multivariate event time

3.3.1 Univariate case

3.3.2 Multivariate case

3.3.3 Empirical results

3.4 High frequency lead/lag

3.4.1 The Hayashi–Yoshida cross-correlation function

3.4.2 Empirical results

3.5 Intraday seasonality of correlation

3.5.1 Empirical results

3.6 Conclusion

Acknowledgment

References

4 Statistical Inference for Volatility and Related Limit Theorems
Nakahiro Yoshida

4.1 Introduction

4.2 QLA for an ergodic diffusion process

4.3 QLA for volatility in the finite time-horizon

4.4 Nonsynchronous covariance estimation

4.4.1 Consistent estimator

4.4.2 Functional limit theorem

4.4.3 Application of YUIMA

4.4.4 Lead–lag estimation

4.5 YUIMA II for statistical analysis and simulation for stochastic differential equations

4.6 Higher order asymptotics and finance

4.6.1 Martingale expansion

4.6.2 Small σ expansion

Acknowledgments

References

PART III MARKET IMPACT

5 Models for the Impact of All Order Book Events
Zoltán Eisler, Jean-Philippe Bouchaud, and Julien Kockelkoren

5.1 Introduction

5.2 A short summary of market order impact models

5.3 Many-event impact models

5.3.1 Notation and definitions

5.3.2 The transient impact model (TIM)

5.3.3 The history dependent impact model (HDIM)

5.4 Model calibration and empirical tests

5.4.1 Data

5.4.2 The case of large ticks

5.4.3 The case of small ticks

5.5 Conclusion

Appendix

Acknowledgments

References

6 Limit Order Flow, Market Impact, and Optimal Order Sizes: Evidence from NASDAQ TotalView-ITCH Data
Nikolaus Hautsch and Ruihong Huang

6.1 Introduction

6.2 Market environment and data

6.3 Major order flow and order book characteristics

6.4 An econometric model for the market impact of limit orders

6.4.1 A cointegrated VAR model for the limit order book

6.4.2 Estimating market impact

6.5 Market impact at NASDAQ

6.6 Optimal order size

6.7 Conclusions

Acknowledgment

References

PART IV OPTIMAL TRADING

Introduction: Trading and Market Micro-structure
Charles-Albert Lehalle

References

7 Collective Portfolio Optimization in Brokerage Data: The Role of Transaction Cost Structure
Damien Challet and David Morton de Lachapelle

7.1 Introduction

7.2 Description of the data

7.3 Results

7.4 The influence of transaction costs on trading behaviour from optimal mean-variance portfolios

7.5 Discussion and outlook

Acknowledgments

References

8 Optimal Execution of Portfolio Transactions with Short-Term Alpha
Adriana M. Criscuolo and Henri Waelbroeck

8.1 Introduction

8.2 Short-term alpha decay and hidden order arbitrage theory

8.3 Total cost definition and constraints

8.3.1 Equations without the risk term

8.3.2 Equations including risk without the alpha term

8.4 Total cost optimization

8.4.1 Results for λ = 0 and the arbitrary alpha term

8.4.2 Risk-adjusted optimization

8.5 Conclusions

8.5.1 Main results in the absence of short-term alpha

8.5.2 Main results with short-term alpha

8.5.3 Institutional trading practices

Proviso

References

Combined References

Index