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On the predictability of energy commodity markets by an entropy-based computational method

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  • Benedetto, F.
  • Giunta, G.
  • Mastroeni, L.

Abstract

This paper proposes a novel computational method for assessing the predictability of commodity market time series, by predicting the entropy of the series under investigation. Assessing the predictability of a time series is the first mandatory step in order to further apply low-risk and efficient price forecasting methods. According to conventional entropy-based analysis (where the entropy is always ex-post estimated), high entropy values characterize unpredictable series, while more stable series exhibits lesser entropy values. Here, we predict (i.e. ex-ante) the entropy regarding the future behavior of a series, based on the observation of historical data. Our prediction is performed according to the optimum least squares minimization algorithm, usually used in many computational aspects of management science. Preliminary results, applied to energy commodity futures, show the effectiveness of the proposed method for application to energy market time series.

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  • Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    • Handle: RePEc:eee:eneeco:v:54:y:2016:i:c:p:302-312
    DOI: 10.1016/j.eneco.2015.12.009
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    Cited by:

    1. Loretta Mastroeni & Pierluigi Vellucci, 2016. "“Butterfly Effect" vs Chaos in Energy Futures Markets," Departmental Working Papers of Economics - University 'Roma Tre' 0209, Department of Economics - University Roma Tre.
    2. Wendong Zhu & Dahai Li & Limin Han, 2022. "Spatial–Temporal Evolution and Sustainable Type Division of Fishery Science and Technology Innovation Efficiency in China," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    3. Fernandes, Leonardo H.S. & de Araujo, Fernando H.A. & Silva, José W.L. & Tabak, Benjamin Miranda, 2022. "Booms in commodities price: Assessing disorder and similarity over economic cycles," Resources Policy, Elsevier, vol. 79(C).
    4. Anis Hoayek & Hassan Hamie & Hans Auer, 2020. "Modeling the Price Stability and Predictability of Post Liberalized Gas Markets Using the Theory of Information," Energies, MDPI, vol. 13(11), pages 1-20, June.
    5. Loretta Mastroeni & Alessandro Mazzoccoli & Greta Quaresima & Pierluigi Vellucci, 2021. "Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect," Papers 2104.11891, arXiv.org, revised Mar 2022.
    6. Loretta Mastroeni & Pierluigi Vellucci, 2022. "Construction of an SDE Model from Intraday Copper Futures Prices," Risks, MDPI, vol. 10(11), pages 1-21, November.
    7. Anis Hoayek & Hassan Hamie & Hans Auer, 2020. "Modeling the Price Stability and Predictability of Post Liberalized Gas Markets Using the Theory of Information," Post-Print emse-03604655, HAL.
    8. Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Butterfly Effect" vs Chaos in Energy Futures Markets," Papers 1610.05697, arXiv.org.
    9. F. Benedetto & L. Mastroeni & P. Vellucci, 2021. "Modeling the flow of information between financial time-series by an entropy-based approach," Annals of Operations Research, Springer, vol. 299(1), pages 1235-1252, April.
    10. Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Chaos" in energy and commodity markets: a controversial matter," Papers 1611.07432, arXiv.org, revised Mar 2017.
    11. Benedetto, Francesco & Mastroeni, Loretta & Quaresima, Greta & Vellucci, Pierluigi, 2020. "Does OVX affect WTI and Brent oil spot variance? Evidence from an entropy analysis," Energy Economics, Elsevier, vol. 89(C).
    12. Aktham Maghyereh & Hussein Abdoh, 2022. "Global financial crisis versus COVID‐19: Evidence from sentiment analysis," International Finance, Wiley Blackwell, vol. 25(2), pages 218-248, August.
    13. Jiang, Ping-Chuan & Feng, Gen-Fu & Yang, Hao-Chang, 2022. "New measurement of sovereign ESG index," Innovation and Green Development, Elsevier, vol. 1(2).
    14. Loretta Mastroeni & Pierluigi Vellucci, 2017. "“Chaos” In Energy And Commodity Markets: A Controversial Matter," Departmental Working Papers of Economics - University 'Roma Tre' 0218, Department of Economics - University Roma Tre.
    15. Leng, Na & Li, Jiang-Cheng, 2020. "Forecasting the crude oil prices based on Econophysics and Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    16. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta & Vellucci, Pierluigi, 2022. "Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect," Resources Policy, Elsevier, vol. 77(C).
    17. Maghyereh, Aktham & Abdoh, Hussein & Awartani, Basel, 2022. "Have returns and volatilities for financial assets responded to implied volatility during the COVID-19 pandemic?," Journal of Commodity Markets, Elsevier, vol. 26(C).
    18. Li, Jiang-Cheng & Leng, Na & Zhong, Guang-Yan & Wei, Yu & Peng, Jia-Sheng, 2020. "Safe marginal time of crude oil price via escape problem of econophysics," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).

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    More about this item

    Keywords

    Computational methods; Entropy analysis; Market efficiency; Energy commodity markets; Risk management science;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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