This book covers major approaches in high-frequency econometrics. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications.

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.

Focus on theory and application State-of-the-art econometric methods to model financial high-frequency data Presents numerous applications, e.g. volatility and liquidy estimation Discussion of implementation details and illustrations of data properties Includes supplementary material: sn.pub/extras

Autorentext

Nikolaus Hautsch, born 1972, is director of the Institute for Econometrics at the Department of Economics and Business Administration at the Humboldt-Universität 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.

Inhalt
1 Introduction.- 2 Microstructure Foundations.- 3 Empirical Properties of High-Frequency Data.- 4 Financial Point Processes.- 5 Univariate Multiplicative Error Models.- 6 Generalized Multiplicative Error Models.- 7 Vector Multiplicative Error Models.- 8 Modelling High-Frequency Volatility.- 9 Estimating Market Liquidity.- 10 Semiparametric Dynamic Proportional Hazard Models.- 11 Univariate Dynamic Intensity Models.- 12 Multivariate Dynamic Intensity Models.- 13 Autoregressive Discrete Processes and Quote Dynamics.- Appendix: Important Distributions for Positive-Value Data.- Index.