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Climate Policy Uncertainty and Crude Oil Market Volatility

Author

Listed:
  • Afees Salisu
  • Philip Omoke
  • Olalekan Fadiya

    (Centre for Econometrics and Applied Research, Ibadan, Nigeria)

Abstract

In this study, we pursue two main innovations. First, we evaluate the predictive value of climate policy uncertainty (CPU) for oil market volatility. Second, we demonstrate how an investor can exploit the information contents of CPU to gain higher returns. We find that increased values of CPU heighten crude oil market risk, while higher forecast gains are achieved in a model that accommodates CPU. We further show that observing CPU offers higher portfolio returns than ignoring it.

Suggested Citation

  • Afees Salisu & Philip Omoke & Olalekan Fadiya, 2023. "Climate Policy Uncertainty and Crude Oil Market Volatility," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 4(1), pages 1-5.
    • Handle: RePEc:ayb:jrnerl:72
    DOI: 2023/03/14
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    References listed on IDEAS

    as
    1. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
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    Cited by:

    1. Afees A. Salisu & Yinka S. Hammed & Ibrahim Ngananga Ouattara, 2023. "Climate Change, Technology Shocks and the US Equity Real Estate Investment Trusts (REITs)," Sustainability, MDPI, vol. 15(19), pages 1-22, October.

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    More about this item

    Keywords

    Climate policy uncertainty; Crude oil market volatility; Profit maximization strategies; Forecast evaluation;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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