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The relationship between oil prices and the indices of renewable energy and technology companies based on QQR and GCQ techniques

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  • Si Mohammed, K.
  • Mellit, A.

Abstract

This paper aims to examine the relationship between oil prices and the indices of renewable energy and technology companies using Quantile Regression (QR.), Quantile-on-Quantile Regression (QQR), and Granger-Causality in Quantiles (GCQ) techniques. The results show strong evidence of an asymmetric effect at the sum of oil prices and technology companies' index on clean energy, indicating that the relationship is vital when these variables decrease than increase. The main finding of the QQR method demonstrates that the impact of oil prices and the technology stock index on clean energy is higher at the upper quantile than the lower quantile. We also establish bi-directional GCQ indicating the considerable change in oil price led to a change in renewable energy; besides, the outcome of the change in renewable energy does not support the causal link to the variation in oil prices across lower extreme quantile at 1%. Finally, our findings support the bi-directional causality from the tech companies market to the conventional and unconventional energy markets. Results identify the substantial role of renewable energy, with implications for global policy, decision-makers, and fund managers.

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  • Si Mohammed, K. & Mellit, A., 2023. "The relationship between oil prices and the indices of renewable energy and technology companies based on QQR and GCQ techniques," Renewable Energy, Elsevier, vol. 209(C), pages 97-105.
    • Handle: RePEc:eee:renene:v:209:y:2023:i:c:p:97-105
    DOI: 10.1016/j.renene.2023.03.123
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