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Tail dependence for heavy-tailed scale mixtures of multivariate distributions
Haijun Li and Yannan Sun
Source: J. Appl. Probab. Volume 46, Number 4
(2009), 925-937.
Abstract
The tail dependence of multivariate distributions is frequently studied via the tool of copulas. In this paper we develop a general method, which is based on multivariate regular variation, to evaluate the tail dependence of heavy-tailed scale mixtures of multivariate distributions, whose copulas are not explicitly accessible. Tractable formulae for tail dependence parameters are derived, and a sufficient condition under which the parameters are monotone with respect to the heavy tail index is obtained. The multivariate elliptical distributions are discussed to illustrate the results.
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Keywords: Tail dependence; heavy tail; multivariate regular variation; copula; hazard rate ordering; multivariate elliptical distribution
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Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.jap/1261670680
Digital Object Identifier: doi:10.1239/jap/1261670680
Zentralblatt MATH identifier: 1179.62076
Mathematical Reviews number (MathSciNet): MR2582698
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