Estimating tail dependence of elliptical distributions - Claudia Klüppelberg; Gabriel Kuhn; Liang Peng (Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, 2006)
Recently 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.
urn:nbn:de:bvb:19-epub-1838-9
Keywords: 519; asymptotic normality; elliptical distribution; regular variation; tail copula; tail dependence function