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. …

, stochastic volatility coupled with fat tails, GARCH and mixture of innovation models. The comparison is based on the accuracy of … volatility specifications, in terms of point forecasting to some degree and density forecasting to a greater degree. …

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

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 …

from the weather forecasting literature known as `ensemble modelling'. In this approach, uncertainty about model … provide two examples of this modelling strategy: (i) forecasting inflation with a disaggregate ensemble; and (ii) forecasting …

This paper develops univariate (ARIMA and ARCH/GARCH) and multivariate models (VAR, VECM and Bayesian VAR) to forecast … forecasting performance of Bayesian VAR models is satisfactory for most interest rates and their superiority in performance is …

, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of …

terms of the forecasting performance of the FCI. Additionally, Bayesian model averaging can improve in specific cases the …