Structured factor copula models for multivariate data

In factor copula models for multivariate data, dependence is modeled via one or several common factors. The models allow great flexibility in modeling different types of dependence structure including tail dependence and asymmetry. We propose two structured factor copula models for the case where variables can be split into non-overlapping groups such that there is homogeneous dependence within each group. A typical example of such variables occurs for stock returns from different sectors.

We use some tail-weighted measures of dependence to select appropriate copulas in the model and to assess the adequacy of fit in the tails. We apply the structured factor copula models to analyze a financial data set, and compare with other copula models for tail inference.