Bias-corrected and robust estimation of the bivariate stable tail dependence function

Abstract

The stable tail dependence function gives a full characterisation of the extremal dependence between two or more random variables. In this paper, we propose an estimator for this function which is robust against outliers in the sample. The estimator is derived from a bivariate second-order tail model together with a proper transformation of the bivariate observations, and its asymptotic properties are studied under some suitable regularity conditions. Our estimation procedure depends on two parameters: $\alpha$, which controls the trade-off between efficiency and robustness of the estimator, and a second-order parameter $\tau$, which can be replaced by a fixed value or by an estimate. In case where $\tau$ has been replaced by the true value or by an external consistent estimator, our robust estimator is asymptotically unbiased, whereas in case where $\tau$ is mis-specified, one loses this property, but still our estimator performs quite well with respect to bias. The finite sample performance of our robust and bias-corrected estimator of the stable tail dependence function is examined on a simulation study involving uncontaminated and contaminated samples. In particular, its behavior is illustrated for different values of the pair $(\alpha , \tau )$ and is compared with alternative estimators from the extreme value literature.