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Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

Abstract

We extend the widely-studied Heterogeneous Autoregressive Realized Volatility (HAR-RV) model to examine the out-of-sample forecasting value of climate-risk factors for the realized volatility of movements of the prices of crude oil, heating oil, and natural gas. We find that climate-risk factors contribute to out-of-sample forecasting performance mainly at a monthly and, in some cases, also at a weekly forecast horizon. We demonstrate that our main finding is robust to various modifications of our forecasting experiment, and to using three different popular shrinkage estimators to estimate the extended HAR-RV model. We also study longer forecast horizons of up to three months, and we account for the possibility that policymakers and forecasters may have an asymmetric loss function.

Suggested Citation

  • Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Working Papers 202175, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202175
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    Cited by:

    1. Theodosios Anastasios Perifanis, 2022. "The Macroeconomic Results of Diligent Resource Revenues Management: The Norwegian Case," Energies, MDPI, vol. 15(4), pages 1-14, February.
    2. Gupta, Rangan & Nielsen, Joshua & Pierdzioch, Christian, 2024. "Stock market bubbles and the realized volatility of oil price returns," Energy Economics, Elsevier, vol. 132(C).
    3. Ren, Yinghua & Wang, Nairong & Zhu, Huiming, 2025. "Dynamic connectedness of climate risks, oil shocks, and China’s energy futures market: Time-frequency evidence from Quantile-on-Quantile regression," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    4. Mengxi He & Yaojie Zhang & Yudong Wang & Danyan Wen, 2024. "Modelling and forecasting crude oil price volatility with climate policy uncertainty," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    5. Fava, Santino Del & Gupta, Rangan & Pierdzioch, Christian & Rognone, Lavinia, 2024. "Forecasting international financial stress: The role of climate risks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
    6. Wang, Kai-Hua & Kan, Jia-Min & Qiu, Lianhong & Xu, Shulin, 2023. "Climate policy uncertainty, oil price and agricultural commodity: From quantile and time perspective," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 256-272.
    7. Siddique, Md. Abubakar & Nobanee, Haitham & Hasan, Md. Bokhtiar & Uddin, Gazi Salah & Hossain, Md. Naiem & Park, Donghyun, 2023. "How do energy markets react to climate policy uncertainty? Fossil vs. renewable and low-carbon energy assets," Energy Economics, Elsevier, vol. 128(C).
    8. Gian Luca Vriz & Luigi Grossi, 2024. "Green bubbles: a four-stage paradigm for detection and propagation," Papers 2410.06564, arXiv.org.
    9. Çepni, Oğuzhan & Gupta, Rangan & Pienaar, Daniel & Pierdzioch, Christian, 2022. "Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty?," Energy Economics, Elsevier, vol. 114(C).
    10. Salisu, Afees A. & Olaniran, Abeeb & Lasisi, Lukman, 2023. "Climate risk and gold," Resources Policy, Elsevier, vol. 82(C).
    11. Guo, Kun & Liu, Fengqi & Sun, Xiaolei & Zhang, Dayong & Ji, Qiang, 2023. "Predicting natural gas futures’ volatility using climate risks," Finance Research Letters, Elsevier, vol. 55(PA).
    12. Xie, Biqing & Xie, Bibo, 2024. "Assessing the impact of climate policy on energy security in developed economies," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 265-282.
    13. Rao, Amar & Lucey, Brian & Kumar, Satish, 2023. "Climate risk and carbon emissions: Examining their impact on key energy markets through asymmetric spillovers," Energy Economics, Elsevier, vol. 126(C).
    14. Wei, Yu & Zhang, Jiahao & Bai, Lan & Wang, Yizhi, 2023. "Connectedness among El Niño-Southern Oscillation, carbon emission allowance, crude oil and renewable energy stock markets: Time- and frequency-domain evidence based on TVP-VAR model," Renewable Energy, Elsevier, vol. 202(C), pages 289-309.

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    Keywords

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    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
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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