Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171916
Authors: 
Ivanov, Eugen
Min, Aleksey
Ramsauer, Franz
Year of Publication: 
2017
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 5 [Year:] 2017 [Issue:] 2 [Pages:] 1-24
Abstract: 
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.
Subjects: 
COPAR model
dynamic factor model
multivariate time series
optimal mean-variance portfolio
vine copula
JEL: 
C58
C53
C10
G10
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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