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. …

-known GARCH models for density prediction of daily S&P 500 and Nikkei 225 index returns, a comparison is provided between …

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 …

Various GARCH models are applied to daily returns of more than 1200 constituents of major stock indices worldwide. The … conclusions. First, an asymmetric GARCH specification is essential when forecasting the 95% value-at-risk. Second, for both the 95 …

We investigate the time-variation of the cross-sectional distribution of asymmetric GARCH model parameters over the S …&P 500 constituents for the period 2000-2012. We find the following results. First, the unconditional variances in the GARCH … unconditional mean that increases. Particularly in the latest financial crisis, the estimated models tend to Integrated GARCH models …

Using GARCH models for density prediction of stock index returns, a comparison is provided between frequentist and …

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 …

basis–for commonly used GARCH models in a large-scale study, using more than twelve years (2000–2012) of daily returns for … difference in performance between updating the parameter estimates of the GARCH equation at a daily or weekly frequency, whereas …