  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.
