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The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning

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  • Sami Ben Jabeur

    (ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University), UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University))

  • Rabeh Khalfaoui

    (ICN Business School)

  • Wissal Ben Arfi

    (EDC - EDC Paris Business School)

Abstract

No abstract is available for this item.

Suggested Citation

  • Sami Ben Jabeur & Rabeh Khalfaoui & Wissal Ben Arfi, 2021. "The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning," Post-Print hal-03797577, HAL.
    • Handle: RePEc:hal:journl:hal-03797577
    DOI: 10.1016/j.jenvman.2021.113511
    Note: View the original document on HAL open archive server: https://hal.science/hal-03797577
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    References listed on IDEAS

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    1. Bonato, Matteo & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "A note on investor happiness and the predictability of realized volatility of gold," Finance Research Letters, Elsevier, vol. 39(C).
    2. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, vol. 12(10), pages 1-11, May.
    3. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    4. Zolfaghari, Mehdi & Ghoddusi, Hamed & Faghihian, Fatemeh, 2020. "Volatility spillovers for energy prices: A diagonal BEKK approach," Energy Economics, Elsevier, vol. 92(C).
    5. Bašta, Milan & Molnár, Peter, 2018. "Oil market volatility and stock market volatility," Finance Research Letters, Elsevier, vol. 26(C), pages 204-214.
    6. Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
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    Citations

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    Cited by:

    1. Xu Gong & Mengjie Li & Keqin Guan & Chuanwang Sun, 2023. "Climate change attention and carbon futures return prediction," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1261-1288, September.
    2. Stef, Nicolae & Başağaoğlu, Hakan & Chakraborty, Debaditya & Ben Jabeur, Sami, 2023. "Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models," Energy Economics, Elsevier, vol. 124(C).
    3. Li, Hailing & Li, Yuxin & Zhang, Hua, 2023. "The spillover effects among the traditional energy markets, metal markets and sub-sector clean energy markets," Energy, Elsevier, vol. 275(C).
    4. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023. "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 663-687, August.
    5. Miriam Sosa & Edgar Ortiz & Alejandra Cabello, 2022. "ESG Green Equity Finance Risk and Links in Mexico: Conditional Volatility and Markov Switching Vector Analyses," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(4), pages 1-21, Octubre -.
    6. Guliyev, Hasraddin & Mustafayev, Eldayag, 2022. "Predicting the changes in the WTI crude oil price dynamics using machine learning models," Resources Policy, Elsevier, vol. 77(C).

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