forecasting, however it is robust in the sense that it provides a more accurate estimation of the predictive density in the region …

forecasting volatility model with the most appropriate error distribution. The results suggest the presence of leverage effect … validate this result. The last twenty eight days out-of-sample forecast adjudged Power-GARCH (1, 1, 1) in student's t error … forecasting model that could guarantee a sound policy decisions. …

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

We develop efficient simulation techniques for Bayesian inference on switching GARCH models. Our contribution to … space models with particular attention to MS-GARCH models. Our multi-move sampling strategy is based on the Forward … Filtering Backward Sampling (FFBS) applied to an approximation of MS-GARCH. Another important contribution is the use of multi …

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models …. An infinite mixture of multivariate normals is given a flexible Dirichlet process prior. The GARCH functional form enters … posterior simulation and computation of the predictive density. Bayes factors and density forecasts with comparisons to GARCH …

We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH … nearly all series. Finally, we carry out a forecasting exercise to evaluate the usefulness of structural break models …

Efficient posterior simulators for two GARCH models with generalized hyperbolic disturbances are presented. The first … model, GHt-GARCH, is a threshold GARCH with a skewed and heavy-tailed error distribution; in this model, the latent … variables that account for skewness and heavy tails are identically and independently distributed. The second model, ODLV-GARCH …