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Climate Risks and Forecastability of the Realized Volatility of Gold and Other Metal Prices

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 use variants of the Heterogeneous Autoregressive Realized Volatility (HAR-RV) model to examine the out-of-sample predictive value of climate-risk factors for the realized volatility of gold price returns as well as the realized volatility of other metal price returns (Copper, Palladium, Platinum, Silver). We estimate the HAR-RV models using not only ordinary least squares, but also we use three different popular shrinkage estimators. Our main finding is that climate-risk factors improve the accuracy of out-of-sample forecasts prices at a monthly and, in some cases, also at a weekly forecast horizon.

Suggested Citation

  • Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and Forecastability of the Realized Volatility of Gold and Other Metal Prices," Working Papers 202172, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202172
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    Cited by:

    1. Ge, Jiamin & Min Du, Anna & Lin, Boqiang, 2025. "“Volatility in a Mug Cup”: Spillovers among cocoa, coffee, sugar futures and the role of climate policy risk," Research in International Business and Finance, Elsevier, vol. 73(PA).
    2. Zhou, Mingtao & Ma, Yong, 2025. "Climate risk and predictability of global stock market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 101(C).
    3. Abdullah, Mohammad & Adeabah, David & Lee, Chi-Chuan & Abakah, Emmanuel Joel Aikins & Bhuiyan, Rubaiyat Ahsan, 2025. "Does climate risk drive digital asset returns?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 666(C).
    4. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta, 2025. "Effects of the climate-related sentiment on agricultural spot prices: Insights from Wavelet Rényi Entropy analysis," Energy Economics, Elsevier, vol. 142(C).
    5. Ma, Yong & Zhou, Mingtao & Li, Shuaibing, 2024. "Weathering market swings: Does climate risk matter for agricultural commodity price predictability?," Journal of Commodity Markets, Elsevier, vol. 36(C).
    6. Yuqin Zhou & Shan Wu & Zhenhua Liu & Lavinia Rognone, 2023. "The asymmetric effects of climate risk on higher-moment connectedness among carbon, energy and metals markets," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    7. Wang, Yu & Cheung, Adrian Wai Kong & Yan, Wan-Lin & Wang, Bin, 2025. "Connectedness of China’s green bond and green stock markets at the low- and high-order moments: The role of economic and climate policy uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 78(C).
    8. Karmakar, Sayar & Gupta, Rangan & Cepni, Oguzhan & Rognone, Lavinia, 2023. "Climate risks and predictability of the trading volume of gold: Evidence from an INGARCH model," Resources Policy, Elsevier, vol. 82(C).
    9. Jiang, Wei & Tang, Wanqing & Li, Jianfeng & Wei, Xiaokun, 2025. "Climate risk and renewable energy market volatility: Machine learning approach," Research in International Business and Finance, Elsevier, vol. 76(C).
    10. Wang, Yiding & Zhao, Xiaojun & Shang, Junyan, 2025. "Dynamic risk spillover in green financial markets: A wavelet frequency analysis from China," Energy Economics, Elsevier, vol. 143(C).
    11. 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).
    12. Dong, Feng & Li, Zhicheng & Huang, Zihuang & Liu, Yu, 2024. "Extreme weather, policy uncertainty, and risk spillovers between energy, financial, and carbon markets," Energy Economics, Elsevier, vol. 137(C).
    13. 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).
    14. Vasilios Plakandaras & Rangan Gupta & Qiang Ji, 2025. "Unraveling Financial Fragility of Global Markets Using Machine Learning," Working Papers 202511, University of Pretoria, Department of Economics.
    15. Chia‐Hsien Tang & Yen‐Hsien Lee & Hung‐Chun Liu & Guan‐Gzhe Zeng, 2024. "Exploring the unpredictable nature of climate policy uncertainty: An empirical analysis of its impact on commodity futures returns in the United States," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1277-1292, July.
    16. Raza, Syed Ali & Khan, Komal Akram, 2024. "Climate policy uncertainty and its relationship with precious metals price volatility: Comparative analysis pre and during COVID-19," Resources Policy, Elsevier, vol. 88(C).
    17. Salisu, Afees A. & Olaniran, Abeeb & Lasisi, Lukman, 2023. "Climate risk and gold," Resources Policy, Elsevier, vol. 82(C).
    18. 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).

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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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