论文标题
有条件的正常极端价值
Conditional Normal Extreme-Value Copulas
论文作者
论文摘要
我们提出了一类新的极值Copulas，这是条件正常模型的极端限制。条件正常模型是条件独立模型的概括，其中使用一个未观察到的因子对观察到的变量之间的依赖性进行建模。在此因素上，这些变量的分布由高斯副群给出。这种结构使人们可以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）构建灵活和简约的模型。我们研究了这些模型的极端价值限制，并显示了提出的Copulas类别的一些有趣的特殊案例。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，我们应用这些Copula模型来分析有关每月风最大值和股票返回最小值的数据。
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.
