We propose a new class of extreme-value copulas which are extreme-value limits of conditional normal models. Conditional normal models are generalizations of conditional independence models, where the dependence among observed variables is modeled using one unobserved factor. Conditional on this factor, the distribution of these variables is given by the Gaussian copula. This structure allows one to build flexible and parsimonious models for data with complex dependence structures, such as data with spatial dependence or factor structure. We study the extreme-value limits of these models and show some interesting special cases of the proposed class of copulas. We develop estimation methods for the proposed models and conduct a simulation study to assess the performance of these algorithms. Finally, we apply these copula models to analyze data on monthly wind maxima and stock return minima.

我们提出了一类新的极值copula，它们是条件法线模型的极值极限。条件正态模型是条件独立性模型的概括，其中使用一个未观察到的因子对观察变量之间的依赖性进行建模。在此因素的条件下，这些变量的分布由高斯copula给出。这种结构允许人们为具有复杂依赖性结构的数据（例如具有空间依赖性或因子结构的数据）建立灵活而简约的模型。我们研究了这些模型的极值极限，并给出了所提出的系数的一些有趣的特殊情况。我们为提出的模型开发估计方法，并进行仿真研究以评估这些算法的性能。最后，