of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1 …,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that … the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model …

Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … outperforms the GARCH model. …

Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … outperforms the GARCH model. …

(GARCH) models, of differing lag and parameter terms, to forecast the variance of the market used in the denominator of the … squared forecast error (MSE) were used to compare the forecasting ability of the ex-ante GARCH models, Artificial Neural …

traditional GARCH-type models (GARCH and GJR-GARCH) the two-regime Markov Switching GARCHtype models (MS-GARCH and MS-GJR-GARCH … persistence of individual markets which substantially differed across the both regimes. Furthermore, the GJR-GARCH and MS-GJR-GARCH … models clearly confirmed the presence of the leverage effect. Consideration of the MS-GARCH-type models enabled to capture …

employed in a bivariate GARCH model, where the joint distribution of the disturbances is split into its marginals and its …-dependent distribution. -- value-at-risk ; copula ; non-normal bivariate GARCH ; asymmetric dependence ; profile likelihood-ratio test …

estimator. The OLS regression with the HAC covariance matrix estimation and the GARCH-type models are employed toexplore the …

correlation multivariate GARCH model. Using daily data from 2001 to 2011, we find that the correlations among stock markets in …

We propose a simultaneous equation system with GARCH errors to model the contemporaneous relations among Asian and …