Measuring and modeling risk using high-frequency data
Wolfgang Härdle,
Nikolaus Hautsch and
Uta Pigorsch
No 2008-045, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk
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 af 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 incorpration and 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)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb649:sfb649dp2008-045
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