Accurate Value-at-Risk forecast with the (good old) normal-GARCH model - Christoph Hartz; Stefan Mittnik; Marc S. Paolella (Goethe University Frankfurt, Center for Financial Studies (CFS), 2006)
A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step, fully data driven. The results for several different financial asset returns over a long out-of-sample forecasting period, as well as use of simulated data, strongly support use of the new method, and the performance is not sensitive to the choice of L.
urn:nbn:de:hebis:30-33294
Keywords: C22; C53; C63; G12; 330; Bootstrap; GARCH; Value-at-Risk