RCAR and CHARMA Models Versus Stochastic Volatility Models: A Forecasting Analysis Using Macrofinancial Data

Abstract

Using inflation and return time series, we first evaluate the forecasting performance of two classes of conditional heteroscedastic models: the random coefficient autoregressive (RCAR) models and the conditional heteroscedastic autoregressive moving average (CHARMA) models. Markov Chain Monte Carlo schemes are developed for the estimation of the model parameters. Furthermore, the forecasting ability of these two models is compared against that of several stochastic volatility models that control for time-varying parameters, in mean effects, leverage effects and moving average errors. We found that our proposed models can produce better point and density forecasts than various stochastic volatility specifications, with the CHARMA models outperforming the RCAR models.