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2008-06-26Buch DOI: 10.18452/4135
Measuring and Modeling Risk Using High-Frequency Data
dc.contributor.authorHärdle, Wolfgang Karl
dc.contributor.authorHautsch, Nikolaus
dc.contributor.authorPigorsch, Uta
dc.date.accessioned2017-06-15T23:44:14Z
dc.date.available2017-06-15T23:44:14Z
dc.date.created2008-07-02
dc.date.issued2008-06-26
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4787
dc.description.abstractMeasuring 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.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjectRealized Volatilityeng
dc.subjectRealized Betaseng
dc.subjectVolatility Modelingeng
dc.subject.ddc330 Wirtschaft
dc.titleMeasuring and Modeling Risk Using High-Frequency Data
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10089730
dc.identifier.doihttp://dx.doi.org/10.18452/4135
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages23
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2008
local.edoc.container-issue45
local.edoc.container-year2008
local.edoc.container-erstkatid2195055-6

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