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Transition risk, physical risk, and the realized volatility of oil and natural gas prices

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  • Salisu, Afees A.
  • Ndako, Umar B.
  • Vo, Xuan Vinh

Abstract

The climate change-related risks have been classified into two namely physical risk and transition risk based on the losses suffered and the response to mitigate and adapt to the paradigm shift in environmental sustainability. Consequently, we test how much predictive value can be exploited from these components of climate risks if they are observed in the pricing of energy commodities that are known to be among the largest emitters of emissions. We note three findings in our empirical analyses in this regard. First, we establish that climate risk influences the volatility of crude oil and natural gas markets and improves their out-of-sample forecasts. Second, the transition climate risk more accurately predicts the out-of-sample forecasts of energy market volatility than the physical climate risk suggesting that investors respond more to the various policies/programmes/initiatives aimed at mitigating the climate change-related losses and fatalities and adapting to the paradigm shift in environmental sustainability than paying attention to the physical damages associated with climate change. Finally, investors are more likely to derive higher economic gains when climate risk is observed than when it is not and the outcome from transition risk is greater than that of physical risk. We further highlight some implications of our findings for investors and policy makers.

Suggested Citation

  • Salisu, Afees A. & Ndako, Umar B. & Vo, Xuan Vinh, 2023. "Transition risk, physical risk, and the realized volatility of oil and natural gas prices," Resources Policy, Elsevier, vol. 81(C).
  • Handle: RePEc:eee:jrpoli:v:81:y:2023:i:c:s0301420723000910
    DOI: 10.1016/j.resourpol.2023.103383
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    Cited by:

    1. Wang Gao & Jiajia Wei & Shixiong Yang, 2023. "The Asymmetric Effects of Extreme Climate Risk Perception on Coal Futures Return Dynamics: Evidence from Nonparametric Causality-In-Quantiles Tests," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    2. Pham, Linh & Kamal, Javed Bin, 2024. "Blessings or curse: How do media climate change concerns affect commodity tail risk spillovers?," Journal of Commodity Markets, Elsevier, vol. 34(C).
    3. Gian Luca Vriz & Luigi Grossi, 2024. "Green bubbles: a four-stage paradigm for detection and propagation," Papers 2410.06564, arXiv.org.

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    More about this item

    Keywords

    Climate risk; Energy market; Realized volatility prediction; Economic gains;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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