In financial and actuarial applications, marginal risks and their dependence structure are often modelled separately. While it is sometimes reasonable to assume that the marginal distributions are ‘known’, it is usually quite involved to obtain information on the copula (dependence structure). Therefore copula models used in practice are quite often only rough guesses. For many purposes, it is thus relevant to know whether certain characteristics derived from \(d\)-variate risks are robust with respect to (at least small) deviations in the copula. In this article, a general concept of copula robustness is introduced and criteria for copula robustness are presented. These criteria are illustrated by means of several examples from quantitative risk management. The concept of aggregation robustness introduced by Embrechts et al. (Finance Stoch. 19:763–790, 2015) can be embedded in our framework of copula robustness.

在金融和精算应用中，边际风险及其依赖结构通常是单独建模的。虽然有时可以合理地假设边际分布是“已知的”，但获取有关 copula（依赖结构）的信息通常相当复杂。因此，实践中使用的 copula 模型通常只是粗略的猜测。因此，出于许多目的，了解某些特征是否源自\(d\)- 变量风险对于 copula 中的（至少很小的）偏差是稳健的。在本文中，介绍了 copula 鲁棒性的一般概念，并提出了 copula 鲁棒性的标准。这些标准通过定量风险管理的几个例子来说明。Embrechts 等人引入的聚合鲁棒性概念。（Finance Stoch. 19:763–790, 2015）可以嵌入到我们的 copula 鲁棒性框架中。