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

Author

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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 Group Munich.
  • Handle: RePEc:ces:ceswps:_6242
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    References listed on IDEAS

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    1. Baumeister, Christiane & Kilian, Lutz & Zhou, Xiaoqing, 2013. "Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis," CEPR Discussion Papers 9572, C.E.P.R. Discussion Papers.
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    4. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    5. Christiane Baumeister & Lutz Kilian, 2014. "What Central Bankers Need To Know About Forecasting Oil Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55, pages 869-889, August.
    6. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
    7. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 326-336, September.
    8. Pagano Patrizio & Pisani Massimiliano, 2009. "Risk-Adjusted Forecasts of Oil Prices," The B.E. Journal of Macroeconomics, De Gruyter, vol. 9(1), pages 1-28, June.
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    Cited by:

    1. repec:eee:eneeco:v:66:y:2017:i:c:p:337-348 is not listed on IDEAS
    2. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    3. Lutz Kilian & Xiaoqing Zhou, 2017. "Modeling Fluctuations in the Global Demand for Commodities," CESifo Working Paper Series 6749, CESifo Group Munich.
    4. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    5. 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.

    More about this item

    Keywords

    Brent oil prices; real-time; combining forecasts; time-variation; and density forecasts;

    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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