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dc.contributor.authorKlüppelberg, Claudiaen
dc.contributor.authorKuhn, Gabrielen
dc.date.accessioned2007-03-06-
dc.date.accessioned2010-05-14T10:14:29Z-
dc.date.available2010-05-14T10:14:29Z-
dc.date.issued2006-
dc.identifier.pidoi:10.5282/ubm/epub.1871en
dc.identifier.piurn:nbn:de:bvb:19-epub-1871-2en
dc.identifier.urihttp://hdl.handle.net/10419/31135-
dc.description.abstractIn this paper we extend the standard approach of correlation structure analysis in order to reduce the dimension of highdimensional statistical data. The classical assumption of a linear model for the distribution of a random vector is replaced by the weaker assumption of a model for the copula. For elliptical copulae a 'correlation-like' structure remains but different margins and non-existence of moments are possible. Moreover, elliptical copulae allow also for a 'copula structure analysis' of dependence in extremes. After introducing the new concepts and deriving some theoretical results we observe in a simulation study the performance of the estimators: the theoretical asymptotic behavior of the statistics can be observed even for a sample of only 100 observations. Finally, we test our method on real financial data and explain differences between our copula based approach and the classical approach. Our new method yields a considerable dimension reduction also in non-linear models.en
dc.language.isoengen
dc.publisher|aLudwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen |cMünchenen
dc.relation.ispartofseries|aDiscussion Paper |x507en
dc.subject.ddc519en
dc.subject.keywordcopula structure analysisen
dc.subject.keywordcorrelation structure analysisen
dc.subject.keywordcovariance structure analysisen
dc.subject.keyworddimension reductionen
dc.subject.keywordelliptical copulaen
dc.subject.keywordfactor analysisen
dc.subject.keywordKendall's tauen
dc.subject.keywordtail copulaen
dc.subject.keywordtail dependenceen
dc.titleCopula structure analysis based on robust and extreme dependence measures-
dc.type|aWorking Paperen
dc.identifier.ppn525325832en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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