Copula-based factor models for multivariate asset returns

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 5 ; Year: 2017 ; Issue: 2 ; Pages: 1-24 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft ; Financial Econometrics ; Forecasting Models; Simulation Methods ; Econometric and Statistical Methods and Methodology: General ; General Financial Markets: General (includes Measurement and Data)
• Subject: COPAR model ; dynamic factor model ; multivariate time series ; optimal mean-variance portfolio ; vine copula

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Ivanov, Eugen ; Min, Aleksey ; Ramsauer, Franz
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2017

Further information

• DOI: doi:10.3390/econometrics5020020
• Handle: http://hdl.handle.net/10419/171916
• Last update: 10.03.2025, 11:42 AM CET