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.

翻译：给出了尾部相依系数非参数估计均方误差的理论表达式，该表达式依赖于一个阈值，该阈值定义了秩数据划分分布尾部的界限。我们提出了一种最优选择该阈值的新方法，将估计的理论均方误差与尾部观测值连接函数（copula）的参数估计相结合。通过模拟，我们将这种半参数方法与文献中提出的其他方法（包括平台搜索算法）进行了比较。