| Summary: | A study was conducted to compare the forecasting performance of four models, namely Stochastic Volatility (SV), Generalized Autoregressive
Conditional Heteroskedasticity (GARCH), Autoregressive with GARCH errors (AR-GARCH) and Autoregressive with SV errors (AR-SV).Bayesian approach and Markov Chain Monte Carlo (MCMC) simulation methods are applied to estimate the parameters of the models and their predictive
densities; using three time series data (daily Euro/US Dollar, British Pound/US Dollar and Iranian Rial/US Dollar exchange rates).Out-ofsample analysis through cumulative predictive Bayes factors clearly showed that modeling regression residuals heteroskedastic, substantially improves predictive performance, especially in turbulent times.A direct comparison
of SV and vanilla GARCH(1,1) indicated that the former performs better in terms of predictive accuracy
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