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

Anne Opschoor, Andre Lucas, István Barra and Dick van Dijk

Journal of Business & Economic Statistics, 2021, vol. 39, issue 4, 1066-1079

Abstract: 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.

Date: 2021
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Citations: View citations in EconPapers (15)

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Working Paper: Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:39:y:2021:i:4:p:1066-1079

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DOI: 10.1080/07350015.2020.1763806

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