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http://hdl.handle.net/10419/32169
  

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dc.contributor.authorChiriac, Roxanaen_US
dc.contributor.authorVoev, Valerien_US
dc.date.accessioned2009-09-17en_US
dc.date.accessioned2010-05-14T12:00:37Z-
dc.date.available2010-05-14T12:00:37Z-
dc.date.issued2008en_US
dc.identifier.piurn:nbn:de:bsz:352-opus-116658-
dc.identifier.urihttp://hdl.handle.net/10419/32169-
dc.description.abstractThis paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model.en_US
dc.language.isoengen_US
dc.publisherCoFE Konstanzen_US
dc.relation.ispartofseriesDiscussion paper series // Zentrum für Finanzen und Ökonometrie, Universität Konstanz 2008,06en_US
dc.subject.jelC32en_US
dc.subject.jelC53en_US
dc.subject.jelG11en_US
dc.subject.ddc330en_US
dc.subject.keywordForecastingen_US
dc.subject.keywordFractional integrationen_US
dc.subject.keywordStochastic dominanceen_US
dc.subject.keywordPortfolio optimizationen_US
dc.subject.keywordRealized covarianceen_US
dc.titleModelling and forecasting multivariate realized volatilityen_US
dc.typeWorking Paperen_US
dc.identifier.ppn608952109en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
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