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arXiv · 2111.11128

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

Matthieu Garcin·Maxime L. D. Nicolas
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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.

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Matthieu Garcin, Maxime L. D. Nicolas. 2021-11-22. Nonparametric estimator of the tail dependence coefficient: balancing bias and variance. https://arxiv.org/abs/2111.11128

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