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2017 | OriginalPaper | Chapter

14. Measuring and Modeling Risk Using High-Frequency Data

Authors : Wolfgang Karl Härdle, N. Hautsch, U. Pigorsch

Published in: Applied Quantitative Finance

Publisher: Springer Berlin Heidelberg

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Abstract

Measuring and modelling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency re- turns. In turn, this so called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures of systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorporation and of the DJIA index.

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Title: Measuring and Modeling Risk Using High-Frequency Data
Authors: Wolfgang Karl Härdle
N. Hautsch
U. Pigorsch
Publisher: Springer Berlin Heidelberg
Book: Applied Quantitative Finance
Print ISBN: 978-3-662-54485-3

Electronic ISBN: 978-3-662-54486-0

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-662-54486-0_14