Extreme dependence for multivariate data
提出两个随机向量之间极端多元依赖的广义概念，基于交叉协方差矩阵的极值性，并给出量化依赖强度的方法，可用于金融变量的压力测试。
This article proposes a generalized notion of extreme multivariate dependence between two random vectors which relies on the extremality of the cross-covariance matrix between these two vectors. Using a partial ordering on the cross-covariance matrices, we also generalize the notion of positive upper dependence. We then propose a means to quantify the strength of the dependence between two given multivariate series and to increase this strength while preserving the marginal distributions. This allows for the design of stress-tests of the dependence between two sets of financial variables that can be useful in portfolio management or derivatives pricing.