论文标题
比较和量化尾巴依赖性
Comparing and quantifying tail dependence
论文作者
论文摘要
我们引入了一个新的随机顺序，以实现随机变量之间的尾巴依赖性。然后，我们研究了尾巴依赖性的不同度量，这些度量是根据拟议的顺序单调的，从而扩展了文献中各种已知的尾巴依赖系数。我们在一项经验研究中运用概念，其中我们研究了不同标准普尔500股票和指数对不同对的尾巴依赖性，并说明了我们尾巴依赖度量比经典尾巴依赖系数的优势。
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.
