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Quantifying information transfer among clean energy, carbon, oil, and precious metals: A novel transfer entropy-based approach

Author

Listed:
  • Zouhaier Dhifaoui

    (SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) - UP1 - Université Paris 1 Panthéon-Sorbonne)

  • Rabeh Khalfaoui

    (ICN Business School)

  • Mohammad Zoynul Abedin
  • Baofeng Shi

Abstract

No abstract is available for this item.

Suggested Citation

  • Zouhaier Dhifaoui & Rabeh Khalfaoui & Mohammad Zoynul Abedin & Baofeng Shi, 2022. "Quantifying information transfer among clean energy, carbon, oil, and precious metals: A novel transfer entropy-based approach," Post-Print hal-03797566, HAL.
    • Handle: RePEc:hal:journl:hal-03797566
    DOI: 10.1016/j.frl.2022.103138
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    Cited by:

    1. Bouteska, Ahmed & Hajek, Petr & Fisher, Ben & Abedin, Mohammad Zoynul, 2023. "Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Yousaf, Imran & Riaz, Yasir & Goodell, John W., 2023. "Integration between asset management tokens, asset management stock, and other financial markets: Evidence from TVP-VAR modeling," Finance Research Letters, Elsevier, vol. 57(C).
    3. Chai, Shanglei & Yang, Xiaoli & Zhang, Zhen & Abedin, Mohammad Zoynul & Lucey, Brian, 2022. "Regional imbalances of market efficiency in China’s pilot emission trading schemes (ETS): A multifractal perspective," Research in International Business and Finance, Elsevier, vol. 63(C).
    4. Liu, Ying Lin & Zhang, Jing Jie & Fang, Yan, 2023. "The driving factors of China's carbon prices: Evidence from using ICEEMDAN-HC method and quantile regression," Finance Research Letters, Elsevier, vol. 54(C).

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