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dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorOkhrin, Ostapen_US
dc.contributor.authorWang, Weiningen_US
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_US
dc.publisherSFB 649, Economic Risk Berlinen_US
dc.relation.ispartofseriesSFB 649 discussion paper 2012-001en_US
dc.subject.keywordHidden Markov modelen_US
dc.subject.keywordHierarchical Archimedean Copulaeen_US
dc.subject.keywordmultivariate distributionen_US
dc.subject.stwMarkovscher Prozessen_US
dc.subject.stwKopula (Mathematik)en_US
dc.subject.stwStatistische Verteilungen_US
dc.titleHMM in dynamic HAC modelsen_US
dc.typeWorking Paperen_US
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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