Abstract A new class of copula models with dynamic dependence is introduced; it can be used when one can assume that there exist a common latent factor that affects all of the observed variables. Conditional on this factor, the distribution of these variables is given by the Gaussian copula with a time-varying correlation matrix, and some observed driving variables can be used to model dynamic correlations. This structure allows one to build flexible and parsimonious models for multivariate data with non-Gaussian dependence that changes over time. The model is computationally tractable in high dimensions and the numerical maximum likelihood estimation is feasible. The proposed class of models is applied to analyze three financial data sets of bond yields, CDS spreads and stock returns. The estimated model is used to construct projected distributions and, for the bond yield and CDS spread datasets, compute the expected maximum number of investments in distress under different scenarios.

中文翻译：

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具有动态依赖性的灵活copula模型，并应用于财务数据

摘要提出了一类具有动态依赖关系的copula模型。当可以假定存在影响所有观察变量的共同潜在因子时，可以使用此方法。在此因素的条件下，这些变量的分布由具有时变相关矩阵的高斯copula给出，并且可以将一些观察到的驱动变量用于建模动态相关。这种结构允许人们为具有非高斯依赖性的随时间变化的多元数据建立灵活而简约的模型。该模型在高维方面在计算上易于处理，并且数值上最大似然估计是可行的。提议的模型类别用于分析债券收益率，CDS利差和股票收益率的三个财务数据集。