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

Beili Zhu

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: 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.

Keywords: Forecasting; oil price; real-time data; time-varying volatility; moving average; stochastic volatility in mean. (search for similar items in EconPapers)
JEL-codes: C11 C53 C82 Q43 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2017-11
New Economics Papers: this item is included in nep-ene, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2017-71

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