Forecasting Portfolio-Value-At-Risk with Nonparametric Lower Tail Dependence Estimates

34 Pages Posted: 27 Jan 2015

See all articles by Karl Siburg

Karl Siburg

Technical University of Dortmund

Pavel Stoimenov

University of Dortmund - Department of Economics

Gregor N. F. Weiss

University of Leipzig - Faculty of Economics and Management Science

Date Written: January 27, 2015

Abstract

We propose to forecast the Value-at-Risk of bivariate portfolios using copulas which are calibrated on the basis of nonparametric sample estimates of the coefficient of lower tail dependence. We compare our proposed method to a conventional copula-GARCH model where the parameter of a Clayton copula is estimated via Canonical Maximum-Likelihood. The superiority of our proposed model is exemplified by analyzing a data sample of nine different bivariate and one nine-dimensional financial portfolio. A comparison of the out-of-sample forecasting accuracy of both models confirms that our model yields economically significantly better Value-at-Risk forecasts than the competing parametric calibration strategy.

Keywords: Copula, tail dependence, nonparametric estimation, Value-at-Risk, Canonical Maximum-Likelihood

JEL Classification: C53, C58

Suggested Citation

Siburg, Karl and Stoimenov, Pavel and Weiss, Gregor N. F., Forecasting Portfolio-Value-At-Risk with Nonparametric Lower Tail Dependence Estimates (January 27, 2015). Journal of Banking and Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2556123

Karl Siburg

Technical University of Dortmund ( email )

Emil-Figge-Straße 50
Dortmund, 44227
Germany

Pavel Stoimenov

University of Dortmund - Department of Economics ( email )

D-44221 Dortmund
Germany

Gregor N. F. Weiss (Contact Author)

University of Leipzig - Faculty of Economics and Management Science ( email )

Grimmaische Str. 12
Leipzig, 04109
Germany
+49 341 97 33821 (Phone)
+49 341 97 33829 (Fax)

HOME PAGE: http://www.wifa.uni-leipzig.de/nfdl

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