Forecasting the Real Prices of Crude Oil: A Dynamic Model Averaging Approach

47 Pages Posted: 6 Apr 2015

See all articles by Yudong Wang

Yudong Wang

Shanghai Jiao Tong University (SJTU)

Chongfeng Wu

Shanghai Jiao Tong University (SJTU) - Aetna School of Management

Li Yang

UNSW Australia Business School, School of Banking and Finance; Financial Research Network (FIRN)

Date Written: April 5, 2015

Abstract

Forecasting oil prices has been of great interests for macroeconomists in the recent years. Our article contributes to this strand of the literature by using a dynamic model averaging (DMA) method to improve forecasting accuracy of real oil prices. The advantage of DMA is that the method combines models in a dynamic way using two forgetting factors to approximate the evolution of model parameters and model switching probabilities, respectively. Our empirical results show that DMA generates more accurate forecasts than the no-change forecasts at the relatively longer horizons. At a horizon of 12 months, the reduction of mean squared prediction error is as high as 30% and the accuracy of directional forecasts increases as high as 71%. It is also found that DMA performs better than Bayesian model averaging, the commonly-used mean combination of forecasts, and more sophisticated individual models such as a time-varying dimension model for the horizons of 3 and 12 months.

Keywords: Forecast; Dynamic model averaging; Time-varying parameter model; Oil prices

JEL Classification: Q43, C53, E32

Suggested Citation

Wang, Yudong and Wu, Chongfeng and Yang, Li, Forecasting the Real Prices of Crude Oil: A Dynamic Model Averaging Approach (April 5, 2015). Available at SSRN: https://ssrn.com/abstract=2590195 or http://dx.doi.org/10.2139/ssrn.2590195

Yudong Wang

Shanghai Jiao Tong University (SJTU)

KoGuan Law School
Shanghai 200030, Shanghai 200052
China

Chongfeng Wu

Shanghai Jiao Tong University (SJTU) - Aetna School of Management ( email )

No.535 Fahuazhen Road
Shanghai, 200052
China

Li Yang (Contact Author)

UNSW Australia Business School, School of Banking and Finance ( email )

Sydney, NSW 2052
Australia
+610293855857 (Phone)

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

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