An introduction to high-frequency finance / / Michel M. Dacorogna ... [et al.]

• Record Nr.: UNINA9910814973103321
• Pubbl/distr/stampa: San Diego, : Academic Press, c2001
• ISBN: 1-282-28475-4; 9786612284755; 0-08-049904-X
• Edizione: [1st edition]
• Descrizione fisica: 1 online resource (411 p.)
• Altri autori (Persone): DacorognaMichel M
• Disciplina: 332.0151955; 330.0151955
• Soggetti: Finance - Econometric models; Time-series analysis
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. 356-375) and index.
• Nota di contenuto: Front Cover; AN INTRODUCTION TO HIGH-FREQUENCY FINANCE; Copyright Page; CONTENTS; LIST OF FIGURES; LIST OF TABLES; PREFACE; ACKNOWLEDGMENTS; CHAPTER 1. INTRODUCTION; 1.1 Markets: The Source of High-Frequency Data; 1.2 Methodology of High-Frequency Research; 1.3 Data Frequency and Market Information; 1.4 New Levels of Significance; 1.5 Interrelating Different Time Scales; CHAPTER 2. MARKETS AND DATA; 2.1 General Remarks on Markets and Data Types; 2.2 Foreign Exchange Markets; 2.3 Over-The-Counter Interest Rate Markets; 2.4 Interest Rate Futures; 2.5 Bond Futures Markets; 2.6 Commodity Futures; 2.7 Equity Markets CHAPTER 3. TIME SERIES of INTEREST; 3.1 Time Series and Operators; 3.2 Variables in Homogeneous Time Series; 3.3 Convolution Operators; 3.4 Microscopic Operators; CHAPTER 4. ADAPTIVE DATA CLEANING; 4.1 Introduction: Using a Filter to Clean the Data; 4.2 Data and Data Errors; 4.3 General Overview of the Filter; 4.4 Basic Filtering Elements and Operations; 4.5 The Scalar Filtering Window; 4.6 The Full-Quote Filtering Window; 4.7 Univariate Filtering; 4.8 Special Filter Elements; 4.9 Behavior and Effects of the Data Filter; CHAPTER 5. BASIC STYLIZED FACTS; 5.1 Introduction; 5.2 Price Formation Process 5.3 Institutional Structure and Exogeneous Impacts; 5.4 Distributional Properties of Returns; 5.5 Scaling Laws; 5.6 Autocorrelation and Seasonality; CHAPTER 6. MODELING SEASONAL VOLATILITY; 6.1 Introduction; 6.2 A Model of Market Activity; 6.3 A New Business Time Scale (ò-Scale); 6.4 Filtering Intraday Seasonalities With Wavelets; CHAPTER 7. REALIZED VOLATILITY DYNAMICS; 7.1 Introduction; 7.2 The Bias of Realized Volatility and Its Correction; 7.3 Conditional Heteroskedasticity; 7.4 The Heterogeneous Market Hypothesis; CHAPTER 8. VOLATILITY PROCESSES; 8.1 Introduction 8.2 Intraday Volatility and GARCH Models; 8.3 Modeling Heterogeneous Volatilities; 8.4 Forecasting Short-Term Volatility; CHAPTER 9. FORECASTING RISK AND RETURN; 9.1 Introduction to Forecasting; 9.2 Forecasting Volatility for Value-at-Risk; 9.3 Forecasting Returns over Multiple Time Horizons; 9.4 Measuring Forecast Quality; CHAPTER 10. CORRELATION AND MULTIVARIATE RISK; 10.1 Introduction; 10.2 Estimating the Dependence of Financial Time Series; 10.3 Covolatility Weighting; 10.4 Stability of Return Correlations; 10.5 Correlation Behavior at High Data Frequencies; 10.6 Conclusions CHAPTER 11. TRADING MODELS; 11.1 Introduction; 11.2 Real-Time Trading Strategies; 11.3 Risk Sensitive Performance Measures; 11.4 Trading Model Algorithms; 11.5 Optimization and Testing Procedures; 11.6 Statistical Study of a Trading Model; 11.7 Trading Model Portfolios; 11.8 Currency Risk Hedging; CHAPTER 12. TOWARD A THEORY of HETEROGENEOUS MARKETS; 12.1 Definition of Efficient Markets; 12.2 Dynamic Markets and Relativistic Effects; 12.3 Impact of the New Technology; 12.4 Zero-Sum Game or Perpetuum Mobile?; 12.5 Discussion of the Conventional Definition; 12.6 An Improved Definition of ""Efficient Markets""
• Sommario/riassunto: Liquid markets generate hundreds or thousands of ticks (the minimum change in price a security can have, either up or down) every business day. Data vendors such as Reuters transmit more than 275,000 prices per day for foreign exchange spot rates alone. Thus, high-frequency data can be a fundamental object of study, as traders make decisions by observing high-frequency or tick-by-tick data. Yet most studies published in financial literature deal with low frequency, regularly spaced data. For a variety of reasons, high-frequency data are becoming a way for understanding market microstructure.