Date: 2012-11-02

Time: 14:30-15:30

Location: BURN 1205

Abstract:

Estimating extreme risks in a multivariate framework is highly connected with the estimation of the extremal dependence structure. This structure can be described via the stable tail dependence function L, for which several estimators have been introduced. Asymptotic normality is available for empirical estimates of L, with rate of convergence k^1/2, where k denotes the number of high order statistics used in the estimation. Choosing a higher k might be interesting for an improved accuracy of the estimation, but may lead to an increased asymptotic bias. We provide a bias correction procedure for the estimation of L. Combining estimators of L is done in such a way that the asymptotic bias term disappears. The new estimator of L is shown to allow more flexibility in the choice of k. Its asymptotic behavior is examined, and a simulation study is provided to assess its small sample behavior. This is a joint work with Cécile Mercadier (Université Lyon 1) and Laurens de Haan (Erasmus University Rotterdam).

Speaker

Anne-Laure Fougères is Professor of Statistics at Université Claude-Bernard, in Lyon, France.