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

2021/11/22 by Garcin, Matthieu, Nicolas, Maxime L. D.
#FOS: Computer and information sciences #FOS: Economics and business #Methodology (stat.ME) #Statistical Finance (q-fin.ST)

paper · doi:10.48550/arxiv.2111.11128

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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