Forecasting portfolio-Value-at-Risk with nonparametric lower tail dependence estimates

Karl Friedrich Siburg, Pavel Stoimenov and Gregor N.F. Weiß

Journal of Banking & Finance, 2015, vol. 54, issue C, 129-140

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 (search for similar items in EconPapers)
JEL-codes: C53 C58 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:54:y:2015:i:c:p:129-140

DOI: 10.1016/j.jbankfin.2015.01.012

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