Nonparametric estimator of the tail dependence coefficient: balancing bias and variance

Matthieu Garcin and Maxime L. D. Nicolas

Papers from arXiv.org

Abstract: A theoretical expression is derived for the mean squared error of a nonparametric estimator of the tail dependence coefficient, depending on a threshold that defines which rank delimits the tails of a distribution. We propose a new method to optimally select this threshold. It combines the theoretical mean squared error of the estimator with a parametric estimation of the copula linking observations in the tails. Using simulations, we compare this semiparametric method with other approaches proposed in the literature, including the plateau-finding algorithm.

Date: 2021-11, Revised 2023-07
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