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Modelling and Forecasting Oil Prices: The Role of Asymmetric Cycles

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  • Jesús Crespo-Cuaresma
  • Adusei Jumah
  • Sohbet Karbuz

Abstract

We propose a new time series model aimed at forecasting crude oil prices. The proposed specification is an unobserved components model with an asymmetric cyclical component. The asymmetric cycle is defined as a sine-cosine wave where the frequency of the cycle depends on past oil price observations. We show that oil price forecasts improve significantly when this asymmetry is explicitly modelled.

Suggested Citation

  • Jesús Crespo-Cuaresma & Adusei Jumah & Sohbet Karbuz, "undated". "Modelling and Forecasting Oil Prices: The Role of Asymmetric Cycles," Working Papers 2007-22, Faculty of Economics and Statistics, Universität Innsbruck.
    • Handle: RePEc:inn:wpaper:2007-22
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    References listed on IDEAS

    as
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    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Knut Anton Mork & Oystein Olsen & Hans Terje Mysen, 1994. "Macroeconomic Responses to Oil Price Increases and Decreases in Seven OECD Countries," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 19-36.
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    Cited by:

    1. Malanichev, A., 2018. "Modelling of Economic Oscillations of Shale Oil Production on the Basis of Analytical Solutions of a Differentiation Equation with a Retarded Argument," Journal of the New Economic Association, New Economic Association, vol. 38(2), pages 54-74.
    2. Prat, Georges & Uctum, Remzi, 2011. "Modelling oil price expectations: Evidence from survey data," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(3), pages 236-247, June.
    3. de Albuquerquemello, Vinícius Phillipe & de Medeiros, Rennan Kertlly & da Nóbrega Besarria, Cássio & Maia, Sinézio Fernandes, 2018. "Forecasting crude oil price: Does exist an optimal econometric model?," Energy, Elsevier, vol. 155(C), pages 578-591.
    4. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
    5. Georges Prat & Remzi Uctum, 2009. "Modelling oil price expectations: evidence from survey data," Working Papers hal-04140866, HAL.
    6. Andreas Breitenfellner & Jesus Crespo Cuaresma, 2008. "Crude Oil Prices and the USD/EUR Exchange Rate," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 4.
    7. Akdoğan, Kurmaş, 2020. "Fundamentals versus speculation in oil market: The role of asymmetries in price adjustment?," Resources Policy, Elsevier, vol. 67(C).
    8. Razek, Noha H.A. & Michieka, Nyakundi M., 2019. "OPEC and non-OPEC production, global demand, and the financialization of oil," Research in International Business and Finance, Elsevier, vol. 50(C), pages 201-225.
    9. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    10. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    11. Jakobsson, Kristofer & Söderbergh, Bengt & Snowden, Simon & Li, Chuan-Zhong & Aleklett, Kjell, 2012. "Oil exploration and perceptions of scarcity: The fallacy of early success," Energy Economics, Elsevier, vol. 34(4), pages 1226-1233.
    12. Jean-Thomas Bernard, Lynda Khalaf, Maral Kichian, and Sebastien McMahon, 2015. "The Convenience Yield and the Informational Content of the Oil Futures Price," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    13. Asche, Frank & Dahl, Roy Endre & Oglend, Atle, 2013. "Value-at-Risk: Risk assessment for the portfolio of oil and gas producers," UiS Working Papers in Economics and Finance 2013/3, University of Stavanger.
    14. Basher, Syed Abul & Raboy, David G., 2018. "The misuse of net present value in energy efficiency standards," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 218-225.

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    More about this item

    Keywords

    Oil price; forecasting; nonlinear time series analysis; asymmetric cycles;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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