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dc.contributor.authorKlüppelberg, Claudiaen_US
dc.contributor.authorKuhn, Gabrielen_US
dc.contributor.authorPeng, Liangen_US
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_US
dc.publisher|aTechn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen|cMünchenen_US
dc.relation.ispartofseries|aDiscussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München|x470en_US
dc.subject.keywordasymptotic normalityen_US
dc.subject.keywordelliptical distributionen_US
dc.subject.keywordregular variationen_US
dc.subject.keywordtail copulaen_US
dc.subject.keywordtail dependence functionen_US
dc.titleEstimating tail dependence of elliptical distributionsen_US
dc.type|aWorking Paperen_US

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