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Climate Risks and Forecasting Stock-Market Returns in Advanced Economies Over a Century

Author

Listed:
  • Mehmet Balcilar

    (Department of Economics, Eastern Mediterranean University, Turkish Republic of Northern Cyprus, Via Mersin 10, Famagusta 99628, Turkey; Department of Economics, OSTIM Technical University, Ankara 06374, Turkey)

  • David Gabauer

    (Data Analysis Systems, Software Competence Center Hagenberg, Hagenberg, Austria)

  • 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

Using monthly data for the eight advanced countries (Canada, France, Germany, Italy, Japan, Switzerland, the United Kingdom (UK), and the United States (US)) over the period from 1916 to 2021, we study whether climate risks have predictive value for stock-market returns. We measure climate risks in terms of both the change in the northern hemisphere temperature anomaly and its volatility and the the change in the global temperature anomaly and its volatility. In our forecasting models, we control for cross-market spillovers of stock-market returns and volatility as well as other risks including oil-price returns and volatility, geopolitical risks, and the gold-to-silver price ratio as a measure of investor risk aversion. Given this large array of control variables, we apply the Lasso estimator to trace out the incremental contribution of climate risks to the predictive performance of our forecasting models. We find that climate risks do not have systematic predictive value for subsequent stock-market returns. Climate risks, however, have short-term out-of-sample predictive value for the connectedness of stock-market returns. Moreover, climate risks have predictive power for stock-market returns when we study historical UK data.

Suggested Citation

  • Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and Forecasting Stock-Market Returns in Advanced Economies Over a Century," Working Papers 202183, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202183
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    2. Rangan Gupta & Anandamayee Majumdar & Christian Pierdzioch & Onur Polat, 2024. "Climate Risks and Real Gold Returns over 750 Years," Forecasting, MDPI, vol. 6(4), pages 1-16, October.
    3. Caporin, Massimiliano & Caraiani, Petre & Cepni, Oguzhan & Gupta, Rangan, 2025. "Predicting the conditional distribution of US stock market systemic Stress: The role of climate risks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 101(C).
    4. Hyder Ali & Salma Naz, 2025. "Forecasting Equity Premium in the Face of Climate Policy Uncertainty," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 513-546, March.
    5. Polat, Onur & Gupta, Rangan & Cepni, Oguzhan & Ji, Qiang, 2024. "Can municipal bonds hedge US state-level climate risks?," Finance Research Letters, Elsevier, vol. 67(PB).
    6. Abeeb Olaniran & Elie Bouri & Rangan Gupta, 2025. "Multi-Moment and Multilayer Analysis of Connectedness among Clean, Brown, and Technology ETFs: The Role of Climate Risk," Working Papers 202519, University of Pretoria, Department of Economics.
    7. Elie Bouri & Rangan Gupta & Asingamaanda Liphadzi & Christian Pierdzioch, 2024. "Forecasting Stock Returns Volatility of the G7 Over Centuries: The Role of Climate Risks," Working Papers 202424, University of Pretoria, Department of Economics.
    8. Foglia, Matteo & Plakandaras, Vasilios & Gupta, Rangan & Ji, Qiang, 2025. "Long-span multi-layer spillovers between moments of advanced equity markets: The role of climate risks," Research in International Business and Finance, Elsevier, vol. 74(C).
    9. Kejin Wu & Sayar Karmakar & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Stock Market Volatility Over a Century in an Emerging Market Economy: The Case of South Africa," Working Papers 202326, University of Pretoria, Department of Economics.

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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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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