We develop new multi-factor dynamic copula models with time-varying factor loadings and observation-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation matrices. A closed-form likelihood expression allows for straightforward parameter estimation and likelihood inference. We apply the new model to a large panel of 100 U.S. stocks over the period 2001–2014. The proposed multi-factor structure is much better than existing (single-factor) models at describing stock return dependence dynamics in high-dimensions. The new factor models also improve one-step-ahead copula density forecasts and global minimum variance portfolio performance. Finally, we investigate different mechanisms to allocate firms into groups and find that a simple industry classification outperforms alternatives based on observable risk factors, such as size, value, or momentum.

Additional Metadata
Keywords Factor copulas, Factor structure, Multivariate density forecast, Score-driven dynamics
Persistent URL dx.doi.org/10.1080/07350015.2020.1763806, hdl.handle.net/1765/128232
Journal Journal of Business and Economic Statistics
Opschoor, A, Lucas, A, Barra, I. (István), & van Dijk, D.J.C. (2020). Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings. Journal of Business and Economic Statistics. doi:10.1080/07350015.2020.1763806