Nikolaus Hautsch

Econometrics of Financial High-Frequency Data

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-642-42772-5
• 2012
• Seitenzahl: 388
• Erscheinungstermin: 29. November 2013
• Englisch
• Abmessung: 235mm x 155mm x 21mm
• Gewicht: 587g
• ISBN-13: 9783642427725
• ISBN-10: 3642427723
• Artikelnr.: 39917003

Autorenporträt

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.

Inhaltsangabe

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.