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Forecasting oil prices: New approaches

Author

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  • Kertlly de Medeiros, Rennan
  • da Nóbrega Besarria, Cássio
  • Pitta de Jesus, Diego
  • Phillipe de Albuquerquemello, Vinicius

Abstract

This paper proposes alternative methodologies for oil price forecasting using mixed-frequency data and a textual sentiment indicator. The latter variable was extracted from oil market reports issued by the Energy Information Administration. We used the root mean square error (RMSE) to evaluate the forecasting accuracy of the econometric models. Compared with other econometric models, the mixed data sampling (MIDAS) model with high-frequency financial indicators and the sentiment index as explanatory variables performs better for forecasting crude oil prices.

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  • Kertlly de Medeiros, Rennan & da Nóbrega Besarria, Cássio & Pitta de Jesus, Diego & Phillipe de Albuquerquemello, Vinicius, 2022. "Forecasting oil prices: New approaches," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022167
    DOI: 10.1016/j.energy.2021.121968
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    2. de Medeiros, Rennan Kertlly & da Silva Bejarano Aragón, Edilean Kleber & Besarria, Cássio da Nóbrega, 2023. "Effects of oil market sentiment on macroeconomic variables," Resources Policy, Elsevier, vol. 83(C).
    3. Wu, Wei & Xu, Meiqi & Su, Ruiqian & Ullah, Kaleem, 2024. "Modeling crude oil volatility using economic sentiment analysis and opinion mining of investors via deep learning and machine learning models," Energy, Elsevier, vol. 289(C).
    4. Yang, Cheng-Hu & Wang, Hai-Tang & Ma, Xin & Talluri, Srinivas, 2023. "A data-driven newsvendor problem: A high-dimensional and mixed-frequency method," International Journal of Production Economics, Elsevier, vol. 266(C).
    5. Zhao, Geya & Xue, Minggao & Cheng, Li, 2023. "A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial–temporal graph neural network," Resources Policy, Elsevier, vol. 85(PB).

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    More about this item

    Keywords

    Forecasting oil prices; Time series; Econometrics; MIDAS model; Sentiment index;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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