Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case

Claudia Klüppelberg, Gabriel Kuhn and Liang Peng

Scandinavian Journal of Statistics, 2008, vol. 35, issue 4, 701-718

Abstract: Abstract. In general, the risk of joint extreme outcomes in financial markets can be expressed as a function of the tail dependence function of a high‐dimensional vector after standardizing marginals. Hence, it is of importance to model and estimate tail dependence functions. Even for moderate dimension, non‐parametrically estimating a tail dependence function is very inefficient and fitting a parametric model to tail dependence functions is not robust. In this paper, we propose a semi‐parametric model for (asymptotically dependent) tail dependence functions via an elliptical copula. Under this model assumption, we propose a novel estimator for the tail dependence function, which proves favourable compared to the empirical tail dependence function estimator, both theoretically and empirically.

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:35:y:2008:i:4:p:701-718

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