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dc.contributor.authorHärdle, Wolfgang Karlen
dc.contributor.authorOkhrin, Ostapen
dc.contributor.authorWang, Weiningen
dc.date.accessioned2012-01-05-
dc.date.accessioned2012-04-05T16:19:15Z-
dc.date.available2012-04-05T16:19:15Z-
dc.date.issued2012-
dc.identifier.urihttp://hdl.handle.net/10419/56691-
dc.description.abstractUnderstanding the dynamics of high dimensional non-normal dependency structure is a challenging task. This research aims at attacking this problem by building up a hidden Markov model (HMM) for Hierarchical Archimedean Copulae (HAC), where the HAC represent a wide class of models for high dimensional dependency, and HMM is a statistical technique to describe time varying dynamics. HMM applied to HAC provide flexible modeling for high dimensional non Gaussian time series. Consistency results for both parameters and HAC structures are established in an HMM framework. The model is calibrated to exchange rate data with a VaR application, where the model's performance is compared with other dynamic models, and in the second application we simulate rainfall process.en
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2012-001en
dc.subject.jelC13en
dc.subject.jelC14en
dc.subject.jelG50en
dc.subject.ddc330en
dc.subject.keywordHidden Markov modelen
dc.subject.keywordHierarchical Archimedean Copulaeen
dc.subject.keywordmultivariate distributionen
dc.subject.stwMarkovscher Prozessen
dc.subject.stwKopula (Mathematik)en
dc.subject.stwStatistische Verteilungen
dc.subject.stwTheorieen
dc.titleHMM in dynamic HAC models-
dc.typeWorking Paperen
dc.identifier.ppn682320536en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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