Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/71115
Authors: 
Beckers, Benjamin
Herwartz, Helmut
Seidel, Moritz
Year of Publication: 
2013
Series/Report no.: 
DIW Discussion Papers 1282
Abstract: 
The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of financial returns and port-folio risk. In this paper, we propose an adjustment of GARCH implied conditional value-at-risk and expected shortfall forecasts that exploits the predictive content of uncorrelated, yet dependent model innovations. The adjustment is motivated by non-Gaussian characteristics of model residuals, and is implemented in a semiparametric fashion by means of conditional moments of simulated bivariate standardized copula distributions. We conduct in-sample forecasting comparisons for a set of 18 stock market indices. In total, four competing copula-GARCH models are contrasted against each other on the basis of their one-step ahead forecasting performance. With regard to forecast unbiasedness and precision, especially the Frank-GARCH models provide most conservative risk forecasts and out-perform all rival models.
Subjects: 
Copula distributions
expected shortfall
GARCH
model selection
non-Gaussian innovations
risk forecasting
value-at-risk
JEL: 
C22
C51
C52
C53
G32
Document Type: 
Working Paper

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