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

parsimonious and effective GARCH(1,1) model with Student-<I>t</I> innovations. The estimation procedure is fully automatic and thus …

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

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 …

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

This paper proposes an up-to-date review of estimation strategies available for the Bayesian inference of GARCH … empirical application to S&P index log-returns. Several non-nested GARCH-type models are estimated and combined to predict the …

parsimonious and effective GARCH(1,1) model with Student-<I>t</I> innovations. The estimation procedure is fully automatic and thus …

estimation strategies available for the Bayesian inference of GARCH-type models. The emphasis is put on a novel efficient …-nested GARCH-type models are estimated and combined to predict the distribution of next-day ahead log-returns. …