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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. This book discusses the best mathematical models and tools for dealing with such vast amounts of data. It provides a framework for the analysis, modeling, and inference of high-frequency financial time series. With particular emphasis on forex, 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.