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

Matthieu Garcin () and Maxime L. D. Nicolas
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Matthieu Garcin: Léonard de Vinci Pôle Universitaire, Research center
Maxime L. D. Nicolas: Université Paris I Panthéon-Sorbonne

Statistical Papers, 2024, vol. 65, issue 8, No 4, 4875-4913

Abstract: 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.

Keywords: Tail dependence coefficient; Nonparametric estimation; Copula; Censored likelihood; 62G05; 62G20; 62G32; 62H12 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-024-01582-w

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