Non-Parametric Inference for Bivariate Extreme-Value Copulas

CentER Discussion Paper No. 2004-91

25 Pages Posted: 10 Nov 2004

See all articles by Johan Segers

Johan Segers

Catholic University of Louvain (UCL)

Date Written: September 2004

Abstract

Extreme-value copulas arise as the possible limits of copulas of component-wise maxima of independent, identically distributed samples. The use of bivariate extreme-value copulas is greatly facilitated by their representation in terms of Pickands dependence functions. The two main families of estimators of this dependence function are (variants of) the Pickands estimator and the Caperaa-Fougeres-Genest estimator. In this paper, a unified treatment is given of these two families of estimators, and within these classes those estimators with the minimal asymptotic variance are determined. Main result is the explicit construction of an adaptive, minimum-variance estimator within a class of estimators that encompasses the Caperaa-Fougeres-Genest estimator.

Keywords: Estimator, nonparametric inference

JEL Classification: C13, C14

Suggested Citation

Segers, Johan, Non-Parametric Inference for Bivariate Extreme-Value Copulas (September 2004). CentER Discussion Paper No. 2004-91, Available at SSRN: https://ssrn.com/abstract=616611 or http://dx.doi.org/10.2139/ssrn.616611

Johan Segers (Contact Author)

Catholic University of Louvain (UCL) ( email )

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