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

Author

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  • Matthieu Garcin

    (Léonard de Vinci Pôle Universitaire, Research center)

  • Maxime L. D. Nicolas

    (Université Paris I Panthéon-Sorbonne)

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.

Suggested Citation

  • Matthieu Garcin & Maxime L. D. Nicolas, 2024. "Nonparametric estimator of the tail dependence coefficient: balancing bias and variance," Statistical Papers, Springer, vol. 65(8), pages 4875-4913, October.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:8:d:10.1007_s00362-024-01582-w
    DOI: 10.1007/s00362-024-01582-w
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    References listed on IDEAS

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