Multivariate Dynamic Copula Models: Parameter Estimation and Forecast Evaluation

Item Type Monograph (Working Paper)
Abstract

This paper introduces multivariate dynamic copula models to account for the time-varying dependence structure in asset portfolios. We firstly enhance the fexibility of this structure by modeling regimes with multivariate mixture copulas. In our second approach, we derive dynamic elliptical copulas by applying the dynamic conditional correlation model (DCC) to multivariate elliptical copulas. The best-ranked copulas according to both in-sample fit and out-of-sample forecast performance indicate the importance of accounting for time-variation. The superiority of multivariate dynamic Clayton and Student-t models further highlight that positive tail dependence as well as the capability of capturing asymmetries in the dependence structure are crucial features of a well-fitting model for an equity portfolio.

Authors Frauendorfer, Karl; Paraschiv, Florentina; Aepli, Matthias D. & Füss, Roland
Language English
Keywords Multivariate dynamic copulas; regime-switching copulas; dynamic conditional correlation (DCC) model; forecast performance; tail dependence.
Subjects business studies
HSG Classification contribution to scientific community
Refereed No
Date 2015
Series Name School of Finance Working Paper Series
Number 2015-13
Depositing User Prof. Dr. Roland Füss
Date Deposited 16 Jul 2015 10:25
Last Modified 23 Aug 2016 11:21
URI: https://www.alexandria.unisg.ch/publications/242511

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Citation

Frauendorfer, Karl; Paraschiv, Florentina; Aepli, Matthias D. & Füss, Roland: Multivariate Dynamic Copula Models: Parameter Estimation and Forecast Evaluation. School of Finance Working Paper Series, 2015, 2015-13.

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https://www.alexandria.unisg.ch/id/eprint/242511
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