Comparing and quantifying tail dependence
提出一种新的随机序来比较随机变量间的尾部相依性，并研究在该序下单调的尾部相依性度量，实证中用于分析标普500股票和指数对的尾部相依性，优于经典尾部相依系数。
We introduce a new stochastic order for the tail dependence between random variables. We then study different measures of tail dependence which are monotone in the proposed order, thereby extending various known tail dependence coefficients from the literature. We apply our concepts in an empirical study where we investigate the tail dependence for different pairs of S&P 500 stocks and indices, and illustrate the advantage of our measures of tail dependence over the classical tail dependence coefficient.