Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings
Tinbergen Institute Discussion Paper 2019-013/IV
61 Pages Posted: 5 Nov 2019
Date Written: October 23, 2019
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.
Keywords: factor copulas, factor structure, score-driven dynamics, multivariate density forecast
JEL Classification: C32, C58, G17
Suggested Citation: Suggested Citation