A note on nonparametric estimation of bivariate tail dependence

Bücher Axel ()
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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

Statistics & Risk Modeling, 2014, vol. 31, issue 2, 151-162

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.

Keywords: Asymptotic variance; nonparametric estimation; rank-based inference; tail copula; tail dependence; Asymptotic variance; nonparametric estimation; rank-based inference; tail copula; tail dependence (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/strm-2013-1143

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