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Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks

Author

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  • Jozef Barunik

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic
    Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic)

  • Barbora Malinska

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic)

Abstract

The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month, 3-month, 6-month and 12-month-ahead forecasts obtained from focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.

Suggested Citation

  • Jozef Barunik & Barbora Malinska, 2015. "Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks," Working Papers IES 2015/25, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Nov 2015.
    • Handle: RePEc:fau:wpaper:wp2015_25
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    Cited by:

    1. Zhai, Dongsheng & Zhang, Tianrui & Liang, Guoqiang & Liu, Baoliu, 2025. "Research on crude oil futures price prediction methods: A perspective based on quantum deep learning," Energy, Elsevier, vol. 320(C).
    2. Oguzhan Cepni, Duc Khuong Nguyen, and Ahmet Sensoy, 2022. "News Media and Attention Spillover across Energy Markets: A Powerful Predictor of Crude Oil Futures Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    3. Guo, Lili & Huang, Xinya & Li, Yanjiao & Li, Houjian, 2023. "Forecasting crude oil futures price using machine learning methods: Evidence from China," Energy Economics, Elsevier, vol. 127(PA).
    4. Fanelli, Viviana & Maddalena, Lucia & Musti, Silvana, 2016. "Modelling electricity futures prices using seasonal path-dependent volatility," Applied Energy, Elsevier, vol. 173(C), pages 92-102.
    5. Liang, Qian & Lin, Qingyuan & Guo, Mengzhuo & Lu, Quanying & Zhang, Dayong, 2025. "Forecasting crude oil prices: A Gated Recurrent Unit-based nonlinear Granger Causality model," International Review of Financial Analysis, Elsevier, vol. 102(C).
    6. Bredin, Don & O'Sullivan, Conall & Spencer, Simon, 2021. "Forecasting WTI crude oil futures returns: Does the term structure help?," Energy Economics, Elsevier, vol. 100(C).
    7. Chai, Jian & Lu, Quanying & Hu, Yi & Wang, Shouyang & Lai, Kin Keung & Liu, Hongtao, 2018. "Analysis and Bayes statistical probability inference of crude oil price change point," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 271-283.
    8. Zhang, Wei & Wu, Jiayi & Wang, Shun & Zhang, Yong, 2025. "Examining dynamics: Unraveling the impact of oil price fluctuations on forecasting agricultural futures prices," International Review of Financial Analysis, Elsevier, vol. 97(C).
    9. Butler, Sunil & Kokoszka, Piotr & Miao, Hong & Shang, Han Lin, 2021. "Neural network prediction of crude oil futures using B-splines," Energy Economics, Elsevier, vol. 94(C).
    10. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "A functional time series analysis of forward curves derived from commodity futures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 646-665.
    11. Xiaojun Chen & Yun Shi & Xiaozhou Wang, 2020. "Equilibrium Oil Market Share under the COVID-19 Pandemic," Papers 2007.15265, arXiv.org.
    12. Mei-Teing Chong & Chin-Hong Puah & Shazali Abu Mansor, 2018. "Oil Price Dynamics Forecasting: An Indicator-Pivoted Paradigm," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 307-311.
    13. Bekiroglu, Korkut & Duru, Okan & Gulay, Emrah & Su, Rong & Lagoa, Constantino, 2018. "Predictive analytics of crude oil prices by utilizing the intelligent model search engine," Applied Energy, Elsevier, vol. 228(C), pages 2387-2397.
    14. Yingrui Zhou & Taiyong Li & Jiayi Shi & Zijie Qian, 2019. "A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices," Complexity, Hindawi, vol. 2019, pages 1-15, February.
    15. Wang, Jue & Zhou, Hao & Hong, Tao & Li, Xiang & Wang, Shouyang, 2020. "A multi-granularity heterogeneous combination approach to crude oil price forecasting," Energy Economics, Elsevier, vol. 91(C).
    16. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    17. Mario Figueiredo & Yuri F. Saporito, 2023. "Forecasting the term structure of commodities future prices using machine learning," Digital Finance, Springer, vol. 5(1), pages 57-90, March.
    18. Taiyong Li & Yingrui Zhou & Xinsheng Li & Jiang Wu & Ting He, 2019. "Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors," Energies, MDPI, vol. 12(19), pages 1-25, September.
    19. Pablo Cansado-Bravo & Carlos Rodríguez-Monroy, 2018. "Persistence of Oil Prices in Gas Import Prices and the Resilience of the Oil-Indexation Mechanism. The Case of Spanish Gas Import Prices," Energies, MDPI, vol. 11(12), pages 1-17, December.
    20. Gao, Xiangyun & Fang, Wei & An, Feng & Wang, Yue, 2017. "Detecting method for crude oil price fluctuation mechanism under different periodic time series," Applied Energy, Elsevier, vol. 192(C), pages 201-212.
    21. Mustanen, Dmitri & Maaitah, Ahmad & Mishra, Tapas & Parhi, Mamata, 2022. "The power of investors’ optimism and pessimism in oil market forecasting," Energy Economics, Elsevier, vol. 114(C).
    22. Wang, Jue & Athanasopoulos, George & Hyndman, Rob J. & Wang, Shouyang, 2018. "Crude oil price forecasting based on internet concern using an extreme learning machine," International Journal of Forecasting, Elsevier, vol. 34(4), pages 665-677.

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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