A note on nonparametric estimation of bivariate tail dependence
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Date
2012-07-16
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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 model. A
brief simulation study indicates that the asymptotic conclusions transfer to finite samples.
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Keywords
asymptotic variance, nonparametric estimation, rank-based inference, tail copula, tail dependence