Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/21980
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dc.contributor.authorHafner, Christian M.en_US
dc.contributor.authorHerwartz, Helmuten_US
dc.date.accessioned2009-01-29T14:13:08Z-
dc.date.available2009-01-29T14:13:08Z-
dc.date.issued2004en_US
dc.identifier.piurn:nbn:de:101:1-200911033770-
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 appliedfrequently little is known about their power properties. In this paper we show thata convenient alternative to residual based testing is to specify a multivariate volatilitymodel, such as multivariate GARCH (or BEKK), and construct a Wald test onnoncausality in variance. We compare both approaches to testing causality in variancein terms of asymptotic and finite sample properties. The Wald test is shown tohave superior power properties under a sequence of local alternatives. Furthermore,we show by simulation that the Wald test is quite robust to misspecification of theorder of the BEKK model, but that empirical power decreases substantially whenasymmetries in volatility are ignored.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseries|aEconomics working paper / Christian-Albrechts-Universität Kiel, Department of Economics |x2004,03en_US
dc.subject.jelC52en_US
dc.subject.jelC22en_US
dc.subject.ddc330en_US
dc.subject.keywordcausalityen_US
dc.subject.keywordmultivariate volatilityen_US
dc.subject.keywordlocal poweren_US
dc.subject.stwARCH-Modellen_US
dc.subject.stwKausalanalyseen_US
dc.subject.stwStatistischer Testen_US
dc.subject.stwVarianzanalyseen_US
dc.subject.stwTheorieen_US
dc.titleTesting for Causality in Variance using Multivariate GARCH Modelsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn383903181en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:cauewp:1690-

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