Econometrics of Financial High-Frequency Data

The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity risk, optimal order placement as well as high-frequency volatility. This book provides a state-of-the art overview on the major approaches in high-frequency econometrics, including univariate and multivariate autoregressive conditional mean approaches for different types of high-frequency variables, intensity-based approaches for financial point processes and dynamic factor models. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications to volatility and liquidity estimation, order book modelling and market microstructure analysis.

Nikolaus Hautsch, born 1972, is director of the Institute for Econometrics at the Department of Economics and Business Administration at the Humboldt-Universitat zu Berlin since 2007. His research interests are financial econometrics, empirical finance and multivariate time series analysis. Particular focus is on the econometric modelling of financial high-frequency data, market microstructure analysis as well as volatility and liquidity estimation.