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Forecasting Stock Returns Volatility of the G7 Over Centuries: The Role of Climate Risks

Author

Listed:
  • Elie Bouri

    (School of Business, Lebanese American University, Lebanon)

  • Rangan Gupta

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

  • Asingamaanda Liphadzi

    (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 Ham- burg, Germany)

Abstract

We analyze whether changes in temperature anomalies, and its second, third, and fourth moments, carry valuable information in forecasting historical stock returns volatility of Canada, France, Germany, Italy, Japan, the United Kingdom (UK), and the United States (US), i.e., the G7 countries, after controlling for leverage, skewness and (excess) kurtosis of stock price fluctuations. Using centuries of monthly data, covering the period 1915-2024 for Canada and Italy, 1898-2024 for France, 1870-2024 for Germany, 1914-2024 for Japan, 1693-2024 for the UK, and 1791-2024 for the US, the results show that stock market moments matter more than climate risks for accurately forecasting stock returns volatility. Extended analyses confirm that climate risks are already captured by the moments of stock returns. We discuss the implications of our findings for investment decisions and economic policy.

Suggested Citation

  • 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.
    • Handle: RePEc:pre:wpaper:202424
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    More about this item

    Keywords

    Stock market; Volatility; Forecasting; Moments; Climate risks; G7 countries;
    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
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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