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A note on nonparametric estimation of bivariate tail dependence

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  • Bücher Axel

    (Ruhr-Universität Bochum, Fakultät für Mathematik, Universitätsstraße 150, 44780 Bochum, Germany, and Université catholique de Louvain, Institut de statistique, Voie du Roman Pays 20, 1348 Louvain-la-Neuve, Belgium)

Abstract

Nonparametric estimation of tail dependence can be based on a standardization of the marginals if their cumulative distribution functions are known. In this paper it is shown to be asymptotically more efficient if the additional knowledge of the marginals is ignored and estimators are based on ranks. The discrepancy between the two estimators is shown to be substantial for the popular Clayton and Gumbel–Hougaard models. A brief simulation study indicates that the asymptotic conclusions transfer to finite samples.

Suggested Citation

  • Bücher Axel, 2014. "A note on nonparametric estimation of bivariate tail dependence," Statistics & Risk Modeling, De Gruyter, vol. 31(2), pages 1-12, June.
    • Handle: RePEc:bpj:strimo:v:31:y:2014:i:2:p:12:n:2
    DOI: 10.1515/strm-2013-1143
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    References listed on IDEAS

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