Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/31120 
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dc.contributor.authorKlüppelberg, Claudiaen
dc.contributor.authorKuhn, Gabrielen
dc.contributor.authorPeng, Liangen
dc.date.accessioned2006-04-25-
dc.date.accessioned2010-05-14T10:14:18Z-
dc.date.available2010-05-14T10:14:18Z-
dc.date.issued2006-
dc.identifier.pidoi:10.5282/ubm/epub.1838en
dc.identifier.piurn:nbn:de:bvb:19-epub-1838-9en
dc.identifier.urihttp://hdl.handle.net/10419/31120-
dc.description.abstractRecently there has been an increasing interest in applying elliptical distributions to risk management. Under weak conditions, Hult and Lindskog (2002) showed that a random vector with an elliptical distribution is in the domain of attraction of a multivariate extreme value distribution. In this chapter we study two estimators for the tail dependence function, which are based on extreme value theory and the structure of an elliptical distribution, respectively. After deriving second order regular variation estimates and proving asymptotic normality for both estimators, we show that the estimator based on the structure of an elliptical distribution is better than that based on extreme value theory in terms of both asymptotic variance and optimal asymptotic mean squared error. Our theoretical results are confirmed by a simulation study.en
dc.language.isoengen
dc.publisher|aLudwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen |cMünchenen
dc.relation.ispartofseries|aDiscussion Paper |x470en
dc.subject.ddc519en
dc.subject.keywordasymptotic normalityen
dc.subject.keywordelliptical distributionen
dc.subject.keywordregular variationen
dc.subject.keywordtail copulaen
dc.subject.keywordtail dependence functionen
dc.titleEstimating tail dependence of elliptical distributions-
dc.type|aWorking Paperen
dc.identifier.ppn510829848en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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