Klüppelberg, Claudia; Kuhn, Gabriel; Peng, Liang Estimating the tail dependence function of an elliptical distribution. (English) Zbl 1111.62048 Bernoulli 13, No. 1, 229-251 (2007). Summary: Recently there has been growing interest in applying elliptical distributions to risk management. Under certain conditions, H. Hult and F. Lindskog [Adv. Appl. Probab. 34, No. 3, 587–608 (2002; Zbl 1023.60021)] show that a random vector with an elliptical distribution is in the domain of attraction of a multivariate extreme value distribution. In this paper we study two estimators for the tail dependence function, which are based on extreme value theory and the structure of an elliptical distribution. 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 simulation study further confirms this. Cited in 1 ReviewCited in 25 Documents MSC: 62H12 Estimation in multivariate analysis 62G32 Statistics of extreme values; tail inference 62G20 Asymptotic properties of nonparametric inference Keywords:asymptotic normality; elliptical distribution; regular variation; tail dependence function Citations:Zbl 1023.60021 PDFBibTeX XMLCite \textit{C. Klüppelberg} et al., Bernoulli 13, No. 1, 229--251 (2007; Zbl 1111.62048) Full Text: DOI