Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings

Anne Opschoor*, André Lucas, István Barra, Dick van Dijk

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1066-1079
Number of pages14
JournalJournal of Business and Economic Statistics
Issue number4
Publication statusPublished - 2021

Bibliographical note

Funding Information:
We thank Andrew Patton, the associate editor, two anonymous referees, David Blaauw, Tijn Wijdogen, and participants at the 10th Annual SoFiE conference and seminar participants at Tinbergen Institute Amsterdam, Lund University, Heidelberg University, Maastricht University, and Vrije Universiteit Amsterdam for helpful comments.

Publisher Copyright:
© 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.


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