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Predictability of the renewable energy market returns: The informational gains from the climate policy uncertainty

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  • Xu, Yongan
  • Li, Ming
  • Yan, Wen
  • Bai, Jiancheng

Abstract

This study investigates the forecasting ability of the Climate Policy Uncertainty (CPU) index on the global renewable energy market returns. We show that CPU is of great excellence positive predictor of the returns of the renewable energy market, and better than the forecasting ability of other predictors. Moreover, we show that a higher CPU index owns more remarkable predictive ability than a lower CPU index and the CPU index is more powerful in predicting the renewable energy market returns after the Paris agreement, which indicates the sensitivity of the renewable energy market to climate policy has dramatically increased. We also find that the predictive power CPU index can enhance the economic gain on asset allocation. Furthermore, we find some similar predictive results on the global renewable energy market in the US NASDAQ OMX Renewable Energy Generation return (GRNREG), Global X Renewable Energy Producers ETT (RNRG), and Renewable Energy Group Inc (REGI) markets.

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  • Xu, Yongan & Li, Ming & Yan, Wen & Bai, Jiancheng, 2022. "Predictability of the renewable energy market returns: The informational gains from the climate policy uncertainty," Resources Policy, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:jrpoli:v:79:y:2022:i:c:s0301420722005840
    DOI: 10.1016/j.resourpol.2022.103141
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    Cited by:

    1. Zhang, Hongwei & Hong, Huojun & Ding, Shijie, 2023. "The role of climate policy uncertainty on the long-term correlation between crude oil and clean energy," Energy, Elsevier, vol. 284(C).
    2. Huthaifa Sameeh Alqaralleh, 2023. "The extreme spillover from climate policy uncertainty to the Chinese sector stock market: wavelet time-varying approach," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-17, December.

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    More about this item

    Keywords

    Climate policy; Renewable energy; Predictability; Uncertainty;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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