Non-Parametric Inference for Bivariate Extreme-Value Copulas

J.J.J. Segers

Research output: Working paperDiscussion paperOther research output

287 Downloads (Pure)

Abstract

Extreme-value copulas arise as the possible limits of copulas of componentwise 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.
Original languageEnglish
Place of PublicationTilburg
PublisherEconometrics
Number of pages26
Volume2004-91
Publication statusPublished - 2004

Publication series

NameCentER Discussion Paper
Volume2004-91

Keywords

  • estimator
  • nonparametric inference

Fingerprint Dive into the research topics of 'Non-Parametric Inference for Bivariate Extreme-Value Copulas'. Together they form a unique fingerprint.

Cite this