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Forecasting the Brent oil price: addressing time-variation in forecast performance

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  • Van Robays, Ine
  • Belu Mănescu, Cristiana

Abstract

This paper demonstrates how the real-time forecasting accuracy of different Brent oil price forecast models changes over time. We find considerable instability in the performance of all models evaluated and argue that relying on average forecasting statistics might hide important information on a model`s forecasting properties. To address this instability, we propose a forecast combination approach to predict quarterly real Brent oil prices. A four-model combination (consisting of futures, risk-adjusted futures, a Bayesian VAR and a DGSE model of the oil market) predicts Brent oil prices more accurately than the futures and the random walk up to 11 quarters ahead, on average, and generates a forecast whose performance is remarkably robust over time. In addition, the model combination reduces the forecast bias and predicts the direction of the oil price changes more accurately than both benchmarks. JEL Classification: Q43, C43, E32

Suggested Citation

  • Van Robays, Ine & Belu Mănescu, Cristiana, 2014. "Forecasting the Brent oil price: addressing time-variation in forecast performance," Working Paper Series 1735, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20141735
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    References listed on IDEAS

    as
    1. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    2. Christiane Baumeister & Lutz Kilian, 2014. "Real-Time Analysis of Oil Price Risks Using Forecast Scenarios," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 62(1), pages 119-145, April.
    3. 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.
    4. 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.
    5. Robert B. Barsky & Lutz Kilian, 2004. "Oil and the Macroeconomy Since the 1970s," Journal of Economic Perspectives, American Economic Association, vol. 18(4), pages 115-134, Fall.
    6. de Menezes, Lilian M. & W. Bunn, Derek & Taylor, James W., 2000. "Review of guidelines for the use of combined forecasts," European Journal of Operational Research, Elsevier, vol. 120(1), pages 190-204, January.
    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. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    9. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    10. Hamilton, James D. & Wu, Jing Cynthia, 2014. "Risk premia in crude oil futures prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 9-37.
    11. Martin Bodenstein & Luca Guerrieri & Lutz Kilian, 2012. "Monetary Policy Responses to Oil Price Fluctuations," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(4), pages 470-504, December.
    12. 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.
    13. 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.
    14. James L. Smith, 2009. "World Oil: Market or Mayhem?," Journal of Economic Perspectives, American Economic Association, vol. 23(3), pages 145-164, Summer.
    15. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
    16. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
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    Citations

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    Cited by:

    1. Michał Rubaszek, 2019. "Forecasting crude oil prices with DSGE models," GRU Working Paper Series GRU_2019_024, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    2. repec:eee:jimfin:v:88:y:2018:i:c:p:54-78 is not listed on IDEAS
    3. repec:fru:finjrn:190301:p:9-21 is not listed on IDEAS
    4. 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.
    5. repec:eee:eneeco:v:76:y:2018:i:c:p:288-302 is not listed on IDEAS
    6. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    7. repec:eee:eneeco:v:66:y:2017:i:c:p:337-348 is not listed on IDEAS
    8. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    9. repec:eee:eneeco:v:76:y:2018:i:c:p:388-402 is not listed on IDEAS
    10. 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; central banks; forecast combination; real-time; time-variation;

    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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