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Forecasting inter-related energy product prices

Author

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  • M. E. Malliaris
  • S. G. Malliaris

Abstract

Five inter-related energy products are forecasted one month into the future using both linear and nonlinear techniques. Both spot prices and data derived from those prices are used as input data in the models. The models are tested by running data from the following year through them. Results show that, even though all products are highly correlated, the prediction results are asymmetric. In forecasts for crude oil, heating oil, gasoline and natural gas, the nonlinear forecasts were best, while for propane, the linear model gave the lowest error.

Suggested Citation

  • M. E. Malliaris & S. G. Malliaris, 2008. "Forecasting inter-related energy product prices," The European Journal of Finance, Taylor & Francis Journals, vol. 14(6), pages 453-468.
    • Handle: RePEc:taf:eurjfi:v:14:y:2008:i:6:p:453-468
    DOI: 10.1080/13518470701705793
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    Cited by:

    1. Pala, Zeydin, 2023. "Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models," Energy, Elsevier, vol. 263(PC).
    2. Li, Jinchao & Wu, Qianqian & Tian, Yu & Fan, Liguo, 2021. "Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network," Energy, Elsevier, vol. 227(C).
    3. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.
    4. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.

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