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dc.contributor.authorHafner, Christian M.en
dc.contributor.authorHerwartz, Helmuten
dc.date.accessioned2009-01-29T14:13:08Z-
dc.date.available2009-01-29T14:13:08Z-
dc.date.issued2004-
dc.identifier.piurn:nbn:de:101:1-200911033770en
dc.identifier.urihttp://hdl.handle.net/10419/21980-
dc.description.abstractTests of causality in variance in multiple time series have been proposed recently, based on residuals of estimated univariate models. Although such tests are applied frequently little is known about their power properties. In this paper we show that a convenient alternative to residual based testing is to specify a multivariate volatility model, such as multivariate GARCH (or BEKK), and construct a Wald test on noncausality in variance. We compare both approaches to testing causality in variance in terms of asymptotic and finite sample properties. The Wald test is shown to have superior power properties under a sequence of local alternatives. Furthermore, we show by simulation that the Wald test is quite robust to misspecification of the order of the BEKK model, but that empirical power decreases substantially when asymmetries in volatility are ignored.en
dc.language.isoengen
dc.publisher|aKiel University, Department of Economics |cKielen
dc.relation.ispartofseries|aEconomics Working Paper |x2004-03en
dc.subject.jelC52en
dc.subject.jelC22en
dc.subject.ddc330en
dc.subject.keywordcausalityen
dc.subject.keywordmultivariate volatilityen
dc.subject.keywordlocal poweren
dc.subject.stwARCH-Modellen
dc.subject.stwKausalanalyseen
dc.subject.stwStatistischer Testen
dc.subject.stwVarianzanalyseen
dc.subject.stwTheorieen
dc.titleTesting for Causality in Variance using Multivariate GARCH Models-
dc.typeWorking Paperen
dc.identifier.ppn383903181en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:cauewp:1690en

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