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Copula-Based Factor Models for Multivariate Asset Returns

Department of Economics, University of Augsburg, Universitätsstr. 16, 86159 Augsburg, Germany
Department of Mathematics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
Author to whom correspondence should be addressed.
Academic Editor: Jean-David Fermanian
Econometrics 2017, 5(2), 20;
Received: 29 September 2016 / Revised: 3 May 2017 / Accepted: 3 May 2017 / Published: 17 May 2017
(This article belongs to the Special Issue Recent Developments in Copula Models)
PDF [389 KB, uploaded 17 May 2017]


Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula autoregressive (COPAR) approach to model the dependence of unobserved multivariate factors resulting from two dynamic factor models. However, the proposed methodology is general and applicable to several factor models as well as to other copula models for stationary multivariate time series. An empirical study illustrates the forecasting superiority of our approach for constructing an optimal portfolio of U.S. industrial stocks in the mean-variance framework. View Full-Text
Keywords: COPAR model; dynamic factor model; multivariate time series; optimal mean-variance portfolio; vine copula COPAR model; dynamic factor model; multivariate time series; optimal mean-variance portfolio; vine copula

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Ivanov, E.; Min, A.; Ramsauer, F. Copula-Based Factor Models for Multivariate Asset Returns. Econometrics 2017, 5, 20.

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