High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds.
Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has
grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements
that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive
book introduces readers to these emerging methods and tools of analysis. Yacine Ait-Sahalia and Jean Jacod cover the mathematical
foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the
asymptotic concepts that their analysis relies on. Ait-Sahalia and Jacod also deal with estimation of the volatility portion
of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions
involving the jump part of the model.
As they demonstrate, the practical importance and relevance of jumps in financial
data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes.
Ait-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining
technical rigor, which makes this book invaluable to researchers and practitioners alike.