In this seminar Beili Zhu will provide an overview of her recent paper, Forecasting the real price of oil under alternative specifications of constant and time-varying volatility.

This study constructs a monthly real-time oil price dataset using backcasting and compares the forecast performance of alternative models of constant and time varying volatility based on the accuracy of point and density forecasts of real oil prices of both real-time and ex-post revised data. The study considers Bayesian autoregressive and autoregressive moving average models with respectively, constant volatility and two forms of time-varying volatility: GARCH and stochastic volatility (SV). 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 found by the author show that time-varying 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.

Beili Zhu is a PhD student scholar at Crawford School of Public Policy. Her current research focuses on forecasting the real oil price and the relationship between real oil price and macroeconomic performances using Bayesian Econometrics methods.

The CAMA Macroeconomics Brown Bag Seminars offer CAMA speakers, in particular PhD students, an opportunity to present their work in progress in front of their peers, and reputable visitors to showcase their work.