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dc.contributor.authorVoev, Valerien_US
dc.date.accessioned2007-04-26en_US
dc.date.accessioned2010-05-14T12:00:40Z-
dc.date.available2010-05-14T12:00:40Z-
dc.date.issued2007en_US
dc.identifier.piurn:nbn:de:bsz:352-opus-32379-
dc.identifier.urihttp://hdl.handle.net/10419/32175-
dc.description.abstractModelling and forecasting the covariance of financial return series has always been a challenge due to the so-called curse of dimensionality. This paper proposes a methodology that is applicable in large dimensional cases and is based on a time series of realized covariance matrices. Some solutions are also presented to the problem of non-positive definite forecasts. This methodology is then compared to some traditional models on the basis of its forecasting performance employing Diebold-Mariano tests. We show that our approach is better suited to capture the dynamic features of volatilities and covolatilities compared to the sample covariance based models.en_US
dc.language.isoengen_US
dc.publisherCoFE Konstanzen_US
dc.relation.ispartofseriesDiscussion paper series // Zentrum für Finanzen und Ökonometrie, Universität Konstanz 2007,01en_US
dc.subject.ddc330en_US
dc.subject.stwVarianzanalyseen_US
dc.subject.stwZeitreihenanalyseen_US
dc.subject.stwKapitalertragen_US
dc.subject.stwPrognoseverfahrenen_US
dc.subject.stwTheorieen_US
dc.titleDynamic modeling of large dimensional covariance matricesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn527906778en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:CoFE-Diskussionspapiere, Universität Konstanz

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