Measuring and Modeling Risk Using High-Frequency Data

Wolfgang Karl Härdle, Nikolaus Hautsch () and Uta Pigorsch

No SFB649DP2008-045, SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

Abstract: Measuring and modeling 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 returns. 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.

Keywords: Realized Volatility; Realized Betas; Volatility Modeling (search for similar items in EconPapers)
JEL-codes: C13 C14 C22 C52 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mst, nep-rmg and nep-upt
Date: Written
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