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Forecasting the Brent Oil Price: Addressing Time-Variation in Forecast Performance

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  • Cristiana Belu Manescu
  • Ine Van Robays

Abstract

This paper explores a range of different forecast methods for Brent oil prices and analyses their performance relative to oil futures and the random walk over the period 1995Q1 - 2015Q2, including periods of stable, upwardly trending and rapidly dropping oil prices. None of the individual methods considered outperforms either benchmark consistently over time or across forecast horizons. To address this instability, we propose a forecast combination for predicting quarterly real Brent oil prices. A four-model combination - consisting of futures, risk-adjusted futures, a Bayesian VAR and a DSGE model of the oil market - predicts oil prices more accurately compared to all methods evaluated up to 11 quarters ahead and generates forecasts whose performance is robust over time. The improvements in forecast accuracy and stability are noticeable in terms of both point forecasts – with MSPE gains of 23% relative to futures at the 11 quarter-ahead horizon and a directional accuracy of 70% – and density forecasts – with CRPS gains of 50% relative to futures and logarithmic score gains of 90%, both at the 7-quarter ahead horizon.

Suggested Citation

  • Cristiana Belu Manescu & Ine Van Robays, 2016. "Forecasting the Brent Oil Price: Addressing Time-Variation in Forecast Performance," CESifo Working Paper Series 6242, CESifo.
  • Handle: RePEc:ces:ceswps:_6242
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    Cited by:

    1. Rubaszek, Michał, 2021. "Forecasting crude oil prices with DSGE models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 531-546.
    2. Diaz, Elena Maria & Pérez-Quirós, Gabriel, 2020. "Daily Tracker of Global Economic Activity. A Close-Up of the Covid-19 Pandemic," CEPR Discussion Papers 15451, C.E.P.R. Discussion Papers.
    3. Kilian, Lutz & Zhou, Xiaoqing, 2018. "Modeling fluctuations in the global demand for commodities," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 54-78.
    4. Knut Are Aastveit & Jamie Cross & Herman K. van Dijk, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Tinbergen Institute Discussion Papers 21-053/III, Tinbergen Institute.
    5. Stavros Degiannakis, George Filis, and Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    6. Roman S. Leukhin, 2019. "Short-Term Fiscal Projections Using Forecast Combination Approach," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 127006, Russia, issue 3, pages 9-21, June.
    7. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2020. "Energy Markets and Global Economic Conditions," CESifo Working Paper Series 8282, CESifo.
    8. Diaz, Elena Maria & Perez-Quiros, Gabriel, 2021. "GEA tracker: A daily indicator of global economic activity," Journal of International Money and Finance, Elsevier, vol. 115(C).
    9. Funk, Christoph, 2018. "Forecasting the real price of oil - Time-variation and forecast combination," Energy Economics, Elsevier, vol. 76(C), pages 288-302.
    10. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    11. Likun Lei & Yaojie Zhang & Yu Wei & Yi Zhang, 2021. "Forecasting the volatility of Chinese stock market: An international volatility index," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1336-1350, January.
    12. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    13. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    14. Knut Are Aastveit & Jamie L. Cross & Herman K. van Dijk, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Working Paper 2021/3, Norges Bank.
    15. Marek Kwas & Michał Rubaszek, 2021. "Forecasting Commodity Prices: Looking for a Benchmark," Forecasting, MDPI, Open Access Journal, vol. 3(2), pages 1-13, June.
    16. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    17. Knut Are Aastveit & Jamie Cross & Herman K. Djik, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Working Papers No 03/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    18. Nida Cakir Melek & Charles W. Calomiris & Harry Mamaysky, 2020. "Mining for Oil Forecasts," Research Working Paper RWP 20-20, Federal Reserve Bank of Kansas City.
    19. Elena CARA & Olga GANCEARUC, 2015. "Forecast Of Brent Oil Price - A Deliberation On Use Of Futures Contracts Or/And Of The Econometric Models Forecasts," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 4(1), pages 18-28, JULY.

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    More about this item

    Keywords

    Brent oil prices; real-time; combining forecasts; time-variation; and density forecasts;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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