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. …

applied within a Bayesian analysisof a GARCH-mixture model which is used for the evaluation of theValue-at-Risk of the return …

ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance densities) areinvestigated in …

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

predicts MA(1) structure with a negative coeffient. Asynchronous updating leads to an MA(1) model for returns with GARCH($1 …,1$) innovations, and predicts a relation between the ARCH and GARCH coefficients. Heterogeneity in memory leads to long … coefficient and the relation between the ARCH and GARCH coefficients for exchange rate data. …

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 …

. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed disturbancedensities …