Forecasting Portfolio-Value-at-Risk with Nonparametric Lower Tail Dependence Estimates
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Date
2013-04-29
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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.
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Keywords
Canonical Maximum-Likelihood, Copula, nonparametric estimation, tail dependence, Value-at-Risk