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Conditional dependence between oil price and stock prices of renewable energy: a vine copula approach

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  • Hanène Mejdoub
  • Ahmed Ghorbel

Abstract

The current paper focusses on the co-movement between oil prices and renewable energy stock markets in a multivariate framework. The vine copula approach that offers a great flexibility in conditional dependence modelling is used. More specifically, we investigate the issue of the average dependence and co-movement between oil prices (West Texas Intermediate [WTI]) and renewable energy stock prices (Wilder Hill New Energy Global Innovation Index [NEX], Wilder Hill Clean Energy Index [ECO] and S and P Global Clean Energy Index [SPGCE]) by applying the vine copula based threshold generalised autoregressive conditional heteroskedasticity (TGARCH) model. Over the period 2003–2016, empirical findings reveal significant and symmetric dependence between the considered markets. Therefore, there is symmetric tail dependence, indicating the evidence of upper and lower tail dependence. This means that movements in oil prices and renewable energy indices are coupled to the same direction. These empirical insights are of particular interest to policymakers, risk managers and investors in renewable energy sector.

Suggested Citation

  • Hanène Mejdoub & Ahmed Ghorbel, 2018. "Conditional dependence between oil price and stock prices of renewable energy: a vine copula approach," Economic and Political Studies, Taylor & Francis Journals, vol. 6(2), pages 176-193, April.
    • Handle: RePEc:taf:repsxx:v:6:y:2018:i:2:p:176-193
    DOI: 10.1080/20954816.2018.1463600
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    Cited by:

    1. Syuhada, Khreshna & Hakim, Arief & Suprijanto, Djoko, 2024. "Assessing systemic risk and connectedness among dirty and clean energy markets from the quantile and expectile perspectives," Energy Economics, Elsevier, vol. 129(C).
    2. Zou, Yingchao & Yu, Lean & Tso, Geoffrey K.F. & He, Kaijian, 2020. "Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    3. Fernanda Fuentes & Rodrigo Herrera, 2020. "Dynamics of Connectedness in Clean Energy Stocks," Energies, MDPI, vol. 13(14), pages 1-19, July.
    4. Maneejuk, Paravee & Kaewtathip, Nuttaphong & Yamaka, Woraphon, 2024. "The influence of the Ukraine-Russia conflict on renewable and fossil energy price cycles," Energy Economics, Elsevier, vol. 129(C).
    5. Yu, Lean & Liang, Shaodong & Chen, Rongda & Lai, Kin Keung, 2022. "Predicting monthly biofuel production using a hybrid ensemble forecasting methodology," International Journal of Forecasting, Elsevier, vol. 38(1), pages 3-20.

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