Journal article

Flexible copula models with dynamic dependence and application to financial data

Pavel Krupskii, Harry Joe



A new class of copula models with dynamic dependence is introduced; it can be used when one can assume that there exist a common latent factor that affects all of the observed variables. Conditional on this factor, the distribution of these variables is given by the Gaussian copula with a time-varying correlation matrix, and some observed driving variables can be used to model dynamic correlations. This structure allows one to build flexible and parsimonious models for multivariate data with non-Gaussian dependence that changes over time. The model is computationally tractable in high dimensions and the numerical maximum likelihood estimation is feasible. The proposed class of models is appl..

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University of Melbourne Researchers


Awarded by Natural Sciences and Engineering Research Council of Canada (NSERC)

Funding Acknowledgements

The authors would like to thank the associate editor and external referee for their constructive comments that helped to improve this paper. The research was supported by funding from the Scotiabank-UBC Risk Analytics Initiative, and the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant 8698.