A g-and-h Copula Approach to Risk Measurement in Multivariate Financial Models

27 Pages Posted: 15 Sep 2010 Last revised: 18 Dec 2010

See all articles by Markus Huggenberger

Markus Huggenberger

University of Mannheim - Department of Risk Theory, Portfolio Management and Insurance

Timo Klett

University of Mannheim - Department of Risk Theory, Portfolio Management and Insurance

Date Written: December 15, 2010

Abstract

We propose and backtest a multivariate Value-at-Risk model for financial returns based on Tukey’s g-and-h distribution. This distributional assumption is especially useful if (conditional) asymmetries as well as heavy tails have to be considered and fast random sampling is of importance. To illustrate our methodology, we fit copula GARCH models with g-and-h distributed residuals to three European stock indices and provide results of out-of-sample Value-at-Risk backtests. We find that our g-and-h model outperforms models with less flexible residual distributions and attains similar results as a benchmark model based on Hansen’s skewed-t distribution.

Keywords: g-and-h distribution, copula, GARCH, Value-at-Risk, stock indices, skewed-t distribution

JEL Classification: C16, C32, C46, C51, G10

Suggested Citation

Huggenberger, Markus and Klett, Timo, A g-and-h Copula Approach to Risk Measurement in Multivariate Financial Models (December 15, 2010). Available at SSRN: https://ssrn.com/abstract=1677431 or http://dx.doi.org/10.2139/ssrn.1677431

Markus Huggenberger (Contact Author)

University of Mannheim - Department of Risk Theory, Portfolio Management and Insurance ( email )

Schloss
D-68131 Mannheim
Germany

Timo Klett

University of Mannheim - Department of Risk Theory, Portfolio Management and Insurance ( email )

Schloss
Mannheim, DE 68131
Germany

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