Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/205303 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
Tinbergen Institute Discussion Paper No. TI 2019-013/IV
Verlag: 
Tinbergen Institute, Amsterdam and Rotterdam
Zusammenfassung: 
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 in- ference. We apply the new model to a large panel of 100 U.S. stocks over the period 2001Ð2014. 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 min- imum variance portfolio performance. Finally, we investigate different mechanisms to allocate firms into groups and find that a simple industry classification outperforms al- ternatives based on observable risk factors, such as size, value or momentum.
Schlagwörter: 
factor copulas
factor structure
score-driven dynamics
multivariate density forecast
JEL: 
C32
C58
G17
Dokumentart: 
Working Paper
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