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Predictability of crude oil prices: An investor perspective

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  • Liu, Li
  • Wang, Yudong
  • Yang, Li

Abstract

We forecast the density of crude oil futures returns using both macroeconomic variables and technical indicators over the period January 1986 through December 2015. The macro variables reflect oil market fundamentals while the technical indicators are constructed based on the popular moving average rules. Several combination strategies over both constant and time-varying parameter models are employed to generate density forecasts. The out-of-sample result shows statistical and economic significance of the predictability. Forecast combination over technical indicators generates more accurate density forecasts than the combination over macro variables. Technical indicators also perform better in terms of Sharpe ratio and certainty equivalent return for risk-averse investors who seek a trade-off between risk and return in the oil market. Technical indicators can better predict oil return density during the expansion period, while macroeconomic variables generate more accurate out-of-sample forecasts during the economic recession period, providing complementary information over the business cycle.

Suggested Citation

  • Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
  • Handle: RePEc:eee:eneeco:v:75:y:2018:i:c:p:193-205
    DOI: 10.1016/j.eneco.2018.08.010
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    18. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
    19. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    20. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.

    More about this item

    Keywords

    Crude oil futures; Density forecasts; Forecast combination; Risk and returns;
    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
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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