Forecasting the real price of oil under alternative specifications of constant and time-varying volatility

This paper constructs a monthly real-time oil price dataset using backcasting and
compares the forecast performance of alternative models of constant and timevarying
volatility based on the accuracy of point and density forecasts of real oil prices of both
real-time and ex-post revised data. The paper considers Bayesian autoregressive and
autoregressive moving average models with respectively, constant volatility and two
forms of time-varying volatility: GARCH and stochastic volatility. In addition to the
standard time-varying models, more flexible models with volatility in mean and moving
average innovations are used to forecast the real price of oil. The results show that timevarying
volatility models dominate their counterparts with constant volatility in terms of
point forecasting at longer horizons and density forecasting at all horizons. The inclusion
of a moving average component provides a substantial improvement in the point and
density forecasting performance for both types of time-varying models while stochastic
volatility in mean is superfluous for forecasting oil prices.