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dc.contributor.authorGribisch, Bastianen
dc.date.accessioned2012-11-15-
dc.date.accessioned2012-11-16T10:16:25Z-
dc.date.available2012-11-16T10:16:25Z-
dc.date.issued2012-
dc.identifier.urihttp://hdl.handle.net/10419/66205-
dc.description.abstractThis paper generalizes the basic Wishart multivariate stochastic volatility model of Philipov and Glickman (2006) and Asai and McAleer (2009) to encompass regime switching behavior. The latent state variable is driven by a first-order Markov process. The model allows for state-dependent (co)variance and correlation levels and state-dependent volatility spillover effects. Parameter estimates are obtained using Bayesian Markov Chain Monte Carlo procedures and filtered estimates of the latent variances and covariances are generated by particle filter techniques. The model is applied to five European stock index return series. The results show that the proposed regime-switching specification substantially improves the in-sample fit and the VaR forecasting performance relative to the basic model.en
dc.language.isoengen
dc.publisher|aKiel University, Department of Economics |cKielen
dc.relation.ispartofseries|aEconomics Working Paper |x2012-14en
dc.subject.jelC32en
dc.subject.jelC58en
dc.subject.jelG17en
dc.subject.ddc330en
dc.subject.keywordMultivariate stochastic volatilityen
dc.subject.keywordDynamic correlationsen
dc.subject.keywordWishart distributionen
dc.subject.keywordMarkov switchingen
dc.subject.keywordMarkov chain Monte Carloen
dc.titleMultivariate wishart stochastic volatility and changes in regime-
dc.typeWorking Paperen
dc.identifier.ppn729892808en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:cauewp:201214en

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