GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the … a GARCH.We compare GDFM+GARCH and standard GARCH performance on two samples up to 171 series, providing one …-step-ahead volatility predictions of returns. The GDFM+GARCH model outperforms the standard GARCH in most cases. These results are robust …

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 …

Renditeverteilung existieren und sich überwiegend auch ein Leverage-Effekt identifizieren lässt. Durch eine ARMA-GARCH …

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

Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance … volatility equation and corresponding value-at-risk predictions. We find that most GARCH coefficients and associated predictions …

, Hong Kong and Taiwan), Japan and US. The originality of the paper is the use of STAR-GARCH models, instead of standard … correlation-cointegration techniques. For each country in the Pacific Basin region, we find statistically adequate STAR-GARCH …