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Combination of forecasts for the price of crude oil on the spot market

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  • Vitor G. Azevedo
  • Lucila M.S. Campos
Abstract
In this paper, we present a combination of three forecast models, ARIMA, exponential smoothing and dynamic regression, in order to predict the West Texas Intermediate (WTI) crude oil spot price and the Brent North Sea (Brent) crude oil spot price. Using samples from the period between January 1994 and June 2012 (in-sample), we identify the parameters and estimate the models. The validated models are combined to perform a forecast out-of-sample between July 2012 and June 2013. The results demonstrate that among the three models tested in-sample with Brent Prices, based on the MAPE measurement error, the ARIMA (2,1,8) model produced the best result, and the dynamic regression model was the best in-sample model for the WTI price. In the validation phase, the dynamic regression models did not prove to be valid, and therefore the combinations are performed only with the ARIMA and exponential smoothing models. For both proxies of oil prices, the combination of forecasts using ARIMA and exponential smoothing (out-of-sample) performed better than individual ARIMA and exponential models and also better than our benchmark models (naive forecast and Neural Network model). Based on the results, it can be inferred that using to a combination of forecasts to predict WTI and Brent spot prices is promising. We also point out that the selected model is easily replicable in spreadsheets and forecasting software and is based only on the past or lagged WTI or Brent values for future predictions.

Suggested Citation

  • Vitor G. Azevedo & Lucila M.S. Campos, 2016. "Combination of forecasts for the price of crude oil on the spot market," International Journal of Production Research, Taylor & Francis Journals, vol. 54(17), pages 5219-5235, September.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:17:p:5219-5235
    DOI: 10.1080/00207543.2016.1162340
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    Citations

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    Cited by:

    1. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    2. Taiyong Li & Yingrui Zhou & Xinsheng Li & Jiang Wu & Ting He, 2019. "Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors," Energies, MDPI, vol. 12(19), pages 1-25, September.
    3. Xiaohang Ren & Wenting Jiang & Qiang Ji & Pengxiang Zhai, 2024. "Seeing is believing: Forecasting crude oil price trend from the perspective of images," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2809-2821, November.
    4. Zhao, Zhengling & Sun, Shaolong & Sun, Jingyun & Wang, Shouyang, 2024. "A novel hybrid model with two-layer multivariate decomposition for crude oil price forecasting," Energy, Elsevier, vol. 288(C).
    5. Ji Wu & Xian Cheng & Stephen Shaoyi Liao, 2020. "Tourism forecast combination using the stochastic frontier analysis technique," Tourism Economics, , vol. 26(7), pages 1086-1107, November.

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