PREFACE

This book presents a unified view of high-frequency time series methods with a particular emphasis on foreign exchange markets as well as interest rate spot and futures markets. The scope of this book is also applicable to other markets, such as equity and commodity markets.

As the archetype of financial markets, the foreign exchange market is the largest financial market worldwide. It involves dealers in different geographic locations, time zones, and working hours who have different time horizons, home currencies, information access, transaction costs, and other institutional constraints. The time horizons vary from intraday dealers, who close their positions every evening, to long-term investors and central banks. In this highly complex and heterogeneous market structure, the market participants are faced with different constraints and use different strategies to reach their financial goals, such as by maximizing their profits or maximizing their utility function after adjusting for market risk.

This book provides a framework to the analysis, modeling, and inference of high-frequency financial time series. It begins with the elementary foundations and definitions needed for studying the fundamental properties of high-frequency financial time series. It extends into the adaptive data-cleaning issues, treatment of seasonal volatility, and modeling of intraday volatility. Fractal properties of the high-frequency financial time series are found and explored, and an intrinsic time is used to construct forecasting models. The book provides a detailed study of how the adopted framework can be effectively utilized to build econometric models of

LIST OF TABLES

5.13 Correlation coefficients between activity measures 167

5.14 Average spreads versus day of the week 171

6.1 Definition of the three generic markets 180

6.2 The #-time parameter estimates for the three generic markets 184

6.3 The volatility ratio for the quality of the #-scale 192 7.1 Difference between lagged correlations 213

8.1 Results of a G ARCH( 1,1) estimation in business time 228

8.2 Results of a GARCH( 1,1) estimation in #-time 229

8.3 Market components of a HARCH process 236

8.4 HARCH coefficients for USD-DEM 240

8.5 Results of the EMA-HARCH for the LIFFE Three-Month

Euromark 242

8.6 Volatility forecasting performance for USD-DEM 246

9.1 The sampling periods of the forecast study 264

9.2 Forecast quality for 10 FX rates against the USD 265

9.3 Forecast quality for 10 FX cross rates 266

9.4 Significance of the forecast quality for 20 FX rates 267

10.1 Correlations from Monte Carlo simulations 275

10.2 Data sampling for correlation as function of time 278

10.3 Mean values, variances, maxima and minima of correlation 279

10.4 Estimation results of the autocorrelation of correlation 288

10.5 Correlation results characterizing the Epps effect 291 11.1 Market time constraints 299 i 1.2 Trading model results versus tree complexity 316 i 1.3 Performance comparison between models 324

11.4 Performance comparison between markets 325

11.5 The best Xeg as a function of opening hours 326

11.6 p-value Comparisons 331

11.7 Random walk Simulations for USD-DEM 332

11.8 GARCH(U) parameter estimates 334 i 1.9 GARCH(U) simulations for USD-DEM 335

11.10 AR(4)-GARCH( 1,1) parameter estimates 337

11.11 AR(4)-GARCH(1,1) simulations for USD-DEM 338

11.12 Portfolio performance of O&A trading models 340

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PREFACE

the price-formation process. Going beyond the price-formation process, the book presents the techniques to construct real-time trading models for financial assets.

It is designed for those who might be starting research in the area as well as for those who are interested in appreciating the statistical and econometric theory that underlies high-frequency financial time series modeling. The targeted audience includes finance professionals, including risk managers and research professionals in the public and private sectors; those taking graduate courses in finance, economics, econometrics, statistics, and time series analysis; and advanced MBA students. Because the high-frequency finance field is relatively new and the literature is scattered in a wide range of academic and nonacademic platforms, this book aims to provide a uniform treatment of the field and an easily accessible platform to high-frequency financial time series analysis - an exciting new field of research.

With the development of this field, a huge new area of research has been initiated, where work has hardly started. This work could not be more fascinating, and a number of discoveries are waiting to be made. We expect research to increase in this field, as people start to understand how these insights can dramatically improve risk-adjusted performances in asset management, market making, and treasury functions and be the foundation for other applications, such as an early warning system of financial markets.

Michel M. Dacorogna Ramazan Gengay Ulrich A. Muller Richard B. Olsen Olivier V. Pictet