Authors: Siburg, Karl Friedrich
Stoimenov, Pavel
Weiß, Gregor N. F.
Title: Forecasting Portfolio-Value-at-Risk with Nonparametric Lower Tail Dependence Estimates
Language (ISO): en
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 financial portfolios. 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.
Subject Headings: Canonical Maximum-Likelihood
nonparametric estimation
tail dependence
Issue Date: 2013-04-29
Appears in Collections:Preprints der Fakultät für Mathematik

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