quarterly basis - for commonly used GARCH models in a large-scale study, using more than twelve years (2000-2012) of daily … following conclusions. First, updating the parameter estimates of the GARCH equation on a daily frequency improves only … overlap, reflecting that the performance is not significantly different. Second, the asymmetric GARCH model with non …

This paper proposes efficient estimators of risk measures in a semiparametric GARCH model defined through moment …

for main indices from stock exchanges was conducted. The VaR forecasts from GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH … volatility trend. However, GARCH-st (1,1) and QML-GARCH(1,1) were found to be the most robust models in the different volatility …

application to VaR and ES forecasts for daily FTSE 100 index returns as generated by AR-GARCH, AR-GJR-GARCH, and AR-HEAVY models …

) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast …

applied within a Bayesian analysisof a GARCH-mixture model which is used for the evaluation of theValue-at-Risk of the return …

-correction method can improve the n-GARCH and n-EGARCH VaR forecasts so much that the acquired VaR predictions are different from the … distribution instead of GARCH improves the performance of the bias-correction method in forecasting the VaR for almost all …

markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance … models, we obtain better Value-at-Risk forecasts compared to GARCH. The quality varies over forecasting horizons and across …

over a small time frame (e.g., a crisis period). We apply our method to test GARCH model specifications for a large panel …