An Introduction to High-Frequency FinanceLiquid 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. This book discusses the best mathematical models and tools for dealing with such vast amounts of data. This book provides a framework for the analysis, modeling, and inference of high frequency financial time series. With particular emphasis on foreign exchange markets, as well as currency, interest rate, and bond futures markets, this unified view of high frequency time series methods investigates the price formation process and concludes by reviewing techniques for constructing systematic trading models for financial assets. |
Contents
| 1 | |
| 10 | |
| 34 | |
CHAPTER 4 ADAPTIVE DATA CLEANING | 82 |
CHAPTER 5 BASIC STYLIZED FACTS | 121 |
CHAPTER 6 MODELING SEASONAL VOLATILITY | 174 |
CHAPTER 7 REALIZED VOLATILITY DYNAMICS | 197 |
CHAPTER 8 VOLATILITY PROCESSES | 219 |
CHAPTER 9 FORECASTING RISK AND RETURN | 248 |
CHAPTER 10 CORRELATION AND MULTIVARIATE RISK | 268 |
CHAPTER 11 TRADING MODELS | 295 |
CHAPTER 12 TOWARD A THEORY of HETEROGENEOUS MARKETS | 348 |
| 356 | |
| 376 | |
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Common terms and phrases
absolute returns activity algorithm analysis applied autocorrelation average behavior bias calculated capital changes Chapter coefficients components computed conditional contracts correlation corresponding credibility currency daily deal defined definition depends discussed distribution effect empirical Equation error estimated example exchange expectation explained fact Figure filter forecast foreign forward frequency function futures FX rates given high-frequency higher horizons important increasing indicators interest interpolation intervals intraday leads limit linear mean measure method moving negative normal observations obtain opening operator optimization parameters performance period points position possible presented problem properties quotes random range rates realized volatility returns risk sample scale seasonality shown shows similar spread standard statistical Table tail term tick trading model transaction USD-DEM values variable variance volatility week window