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Testing the Nonlinear Long- and Short-Run Distributional Asymmetries Effects of Bitcoin Prices on Bitcoin Energy Consumption: New Insights through the QNARDL Model and XGBoost Machine-Learning Tool

Author

Listed:
  • Kais Tissaoui

    (Management Information Systems Department, Applied College, University of Ha’il, Hail City P.O. Box 2440, Saudi Arabia)

  • Taha Zaghdoudi

    (Laboratoire de Recherche en Economie et Finance Appliquées, Carthage High Commercial Studies Institute, University of Carthage, Carthage 1054, Tunisia)

  • Sahbi Boubaker

    (Department of Computer & Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia)

  • Besma Hkiri

    (Department of Finance and Economics, College of Business, University of Jeddah, Jeddah 21959, Saudi Arabia)

  • Mariem Talbi

    (Management Information Systems Department, Applied College, University of Ha’il, Hail City P.O. Box 2440, Saudi Arabia)

Abstract

This study investigates the asymmetric impacts of Bitcoin prices on Bitcoin energy consumption. Two series are shown to be chaotic and non-linear using the BDS Independence test. To take into consideration this nonlinearity, we employed the QNARDL model as a traditional technique and Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) as non-conventional approaches to study the link between Bitcoin energy usage and Bitcoin prices. Referring to QNARDL estimates, results show that the relationship between Bitcoin energy use and prices is asymmetric. Additionally, results demonstrate that changes in Bitcoin prices have a considerable effect, both short- and long-run, on energy consumption. As a result, any upsurge in the price of Bitcoin leads to an immediate boost in energy use. Furthermore, the short-term drop in Bitcoin values causes an increase in energy use. However, higher Bitcoin prices reduce energy use in the long run. Otherwise, every decline in Bitcoin prices leads to a long-term reduction in energy use. In addition, the performance metrics and convergence of the cost function provide evidence that the XGBoost model dominates the SVM model in terms of Bitcoin energy consumption forecasting. In addition, we analyze the effectiveness of several modeling approaches and discover that the XGBoost model (MSE: 0.52%; RMSE: 0.72 and R 2 : 96%) outperforms SVM (MSE: 4.89; RMSE: 2.21 and R 2 : 75%) in predicting. Results indicate that the forecast of Bitcoin energy consumption is more influenced by positive shocks to Bitcoin prices than negative shocks. This study gives insights into the policies that should be implemented, such as increasing the sustainable capacity, efficiency, and flexibility of mining operations, which would allow for the reduction of the negative impacts of Bitcoin price shocks on energy consumption.

Suggested Citation

  • Kais Tissaoui & Taha Zaghdoudi & Sahbi Boubaker & Besma Hkiri & Mariem Talbi, 2024. "Testing the Nonlinear Long- and Short-Run Distributional Asymmetries Effects of Bitcoin Prices on Bitcoin Energy Consumption: New Insights through the QNARDL Model and XGBoost Machine-Learning Tool," Energies, MDPI, vol. 17(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2810-:d:1410957
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    References listed on IDEAS

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