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dc.contributor.authorEngle, Robert F.-
dc.contributor.authorPatton, Andrew J.-
dc.date.accessioned2008-05-29T12:02:30Z-
dc.date.available2008-05-29T12:02:30Z-
dc.date.issued2001-01-29-
dc.identifier.urihttp://hdl.handle.net/2451/26881-
dc.description.abstractA volatility model must be able to forecast volatility; this is the central requirement in almost all financial applications. In this paper we outline some stylised facts about volatility that should be incorporated in a model; pronounced persistence and meanreversion, asymmetry such that the sign of an innovation also affects volatility and the possibility of exogenous or pre-determined variables influencing volatility. We use data on the Dow Jones Industrial index to illustrate these stylised facts, and the ability of GARCH-type models to capture these features. We conclude with some challenges for future research in this area.en
dc.language.isoen_USen
dc.relation.ispartofseriesS-DRP-01-03en
dc.subjectvolatility modellingen
dc.subjectARCHen
dc.subjectGARCHen
dc.subjectvolatility forecastingen
dc.titleWHAT GOOD IS A VOLATILITY MODEL?en
dc.typeWorking Paperen
Appears in Collections:Derivatives Research

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