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  5. Forecasting the volatility of stock returns in the G7 countries over centuries: the role of climate risks

Forecasting the volatility of stock returns in the G7 countries over centuries: the role of climate risks

Publication date
2026-01-26
Document type
Forschungsartikel
Author
Bouri, Elie
Gupta, Rangan
Liphadzi, Asingamaanda
Pierdzioch, Christian  
Organisational unit
VWL, insb. Monetäre Ökonomik  
DOI
10.1007/s12197-026-09751-3
URI
https://openhsu.ub.hsu-hh.de/handle/10.24405/23085
Scopus ID
2-s2.0-105028720221
Publisher
Springer
Series or journal
Journal of Economics and Finance
ISSN
1055-0925
Periodical volume
50
Periodical issue
1
Article ID
12
Part of the university bibliography
✅
Additional Information
Language
English
Keyword
Climate risks
Forecasting
G7 countries
Moments
Stock market
Volatility
Abstract
We investigate whether changes in temperature anomalies, along with their second, third, and fourth statistical moments, can serve as indicators of physical climate risks and provide valuable insights for forecasting historical stock return volatility in Canada, France, Germany, Italy, Japan, the United Kingdom (UK), and the United States (US) the G7 countries. This analysis controls for factors such as leverage, skewness, and excess kurtosis in stock price fluctuations. Using extensive monthly data spanning from 1915 to 2024 for Canada and Italy, from 1898 to 2024 for France, from 1870 to 2024 for Germany, from 1914 to 2024 for Japan, from 1693 to 2024 for the UK, and from 1791 to 2024 for the US, our findings indicate that the moments of stock markets play a more significant role than climate risks in accurately forecasting stock return volatility. Further analysis confirms that the impacts of climate risks are already reflected in the statistical moments of stock returns. We discuss the implications of these findings for investment decisions and economic policy, suggesting that investors and policymakers in the G7 countries should focus more on statistical moments rather than physical climate risks when producing forecasts of stock market volatility for their decision-making processes.
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Published version
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