Forecasting Portfolio-Value-At-Risk with Nonparametric Lower Tail Dependence Estimates
34 Pages Posted: 27 Jan 2015
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
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