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 …

managers. This study is conducted to analyze the volatility clustering and asymmetry occurrence by employing different GARCH … estimated the analytical competence of GARCH models and found that GJR-GARCH and EGARCH executed better results than GARCH (p, q …) in RDCs stock markets. It also shows that GJR-GARCH and EGAECH explain the asymmetric behavior along with an accurate …

identified by generalized autoregressive conditional heteroskedasticity (GARCH) are reviewed and compared in a Monte Carlo study …. The bootstrap methods considered are a wild bootstrap, a moving blocks bootstrap and a GARCH residual based bootstrap …. Estimation is done by Gaussian maximum likelihood, a simplified procedure based on univariate GARCH estimations and a method that …

, namely, GARCH (1,1), GJR (1,1) and EGARCH (1,1), are used to estimate the abnormal rate of change in the number of tourists …

The paper develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and … three US financial assets, we compare the realized MEGARCH models with existing multivariate GARCH class models. The …

-Maximum Likelihood Estimator for a general nonlinear conditional mean model with first-order GARCH errors. …

choosing an optimal Generalized Autoregressive Conditional Heteroskedasticity- (GARCH) type model. Subsequently, inflation and …