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Klüppelberg, Claudia
Kuhn, Gabriel
Peng, Liang
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Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 468
In general, risk of an extreme outcome in financial markets can be expressed as a function of the tail copula of a high-dimensional vector after standardizing marginals. Hence it is of importance to model and estimate tail copulas. Even for moderate dimension, nonparametrically estimating a tail copula is very inefficient and fitting a parametric model to tail copulas is not robust. In this paper we propose a semi-parametric model for tail copulas via an elliptical copula. Based on this model assumption, we propose a novel estimator for the tail copula, which proves favourable compared to the empirical tail copula, both theoretically and empirically
Asymptotic normality
Dependence modeling
Elliptical copula
Elliptical distribution
Multivariate modeling
Regular variation
Tail copula
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Working Paper

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