find that the Poisson jump-diffusion and not the GARCH (1,1) process lends statistical support for the data description. We …

The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of … financial returns and port-folio risk. In this paper, we propose an adjustment of GARCH implied conditional value-at-risk and … comparisons for a set of 18 stock market indices. In total, four competing copula-GARCH models are contrasted against each other …

Using unobservable conditional variance as measure, latent-variable approaches, such as GARCH and stochastic …

processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are … separately. The primary result of the paper is that the volatility is best modelled using a GARCH process and that an ARMA …

Dynamic Factor GARCH (DF-GARCH), is used here to exploit the relations between inflation and the other macroeconomic variables … for inflation forecasting purposes. The DF-GARCH is a dynamic factor model with the additional assumption of conditionally … heteroskedastic dynamic factors. When comparing the Dynamic Factor GARCH with univariate models and with the traditional dynamic …

GARCH models which capture volatility clustering and, therefore, are appropriate to analyse financial market data. Models …

implied conditional variance from a GARCH model as a measure of volatility. Although treating the volatility measure as either …

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 …

GARCH models which capture volatility clustering and, therefore, are appropriate to analyse financial market data. Models …

Using unobservable conditional variance as measure, latentvariable approaches, such as GARCH and stochasticvolatility …