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Exploring the Relationship and Predictive Accuracy for the Tadawul All Share Index, Oil Prices, and Bitcoin Using Copulas and Machine Learning

Author

Listed:
  • Sara Ali Alokley

    (Department of Finance, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Sawssen Araichi

    (Department of Quantitative Methods, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia
    LaREMFiQ, IHEC, University of Sousse, BP No. 40, Sousse 4054, Tunisia)

  • Gadir Alomair

    (Department of Quantitative Methods, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

Abstract

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices.

Suggested Citation

  • Sara Ali Alokley & Sawssen Araichi & Gadir Alomair, 2024. "Exploring the Relationship and Predictive Accuracy for the Tadawul All Share Index, Oil Prices, and Bitcoin Using Copulas and Machine Learning," Energies, MDPI, vol. 17(13), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3241-:d:1427150
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    References listed on IDEAS

    as
    1. Sara Ali Alokley & Mansour Saleh Albarrak, 2020. "Clustering of Extremes in Financial Returns: A Study of Developed and Emerging Markets," JRFM, MDPI, vol. 13(7), pages 1-11, July.
    2. Awartani, Basel & Maghyereh, Aktham Issa, 2013. "Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries," Energy Economics, Elsevier, vol. 36(C), pages 28-42.
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    4. Mohanty, Sunil K. & Nandha, Mohan & Turkistani, Abdullah Q. & Alaitani, Muhammed Y., 2011. "Oil price movements and stock market returns: Evidence from Gulf Cooperation Council (GCC) countries," Global Finance Journal, Elsevier, vol. 22(1), pages 42-55.
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    6. Feng Jin & Jingwei Li & Guangchen Li & Lele Qin, 2022. "Modeling the Linkages between Bitcoin, Gold, Dollar, Crude Oil, and Stock Markets: A GARCH-EVT-Copula Approach," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-10, August.
    7. Naifar, Nader & Al Dohaiman, Mohammed Saleh, 2013. "Nonlinear analysis among crude oil prices, stock markets' return and macroeconomic variables," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 416-431.
    8. Katarzyna Kuziak & Joanna Górka, 2023. "Dependence Analysis for the Energy Sector Based on Energy ETFs," Energies, MDPI, vol. 16(3), pages 1-30, January.
    9. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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