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An analytic framework for assessing the impacts of physical risk through a (climate-related) expected shortfall

Author

Listed:
  • Piluso, Fabio
  • Strano, Eugenia
  • Ceraso, Danilo

Abstract

This paper introduces a novel measure, that is the climate-related Expected Shortfall, employing a quadratic damage function to capture the nonlinear effects of global warming on economic losses. We find a contrasting geographical pattern: as global warming rises, welfare economic losses in Central Europe (Southern) increase, whilst losses at lower southern latitudes decrease due to the nonlinear effect of climate tipping damage. Additionally, we demonstrate that the ES model is a more coherent measure compared to the VaR model, which lacks the subadditivity axiom and overlooks the “hidden” risks. The results offer a forward-looking tool for regulators and policymakers, enhancing our understanding of practical solutions for measuring climate-related financial risks and encouraging further research in this field.

Suggested Citation

  • Piluso, Fabio & Strano, Eugenia & Ceraso, Danilo, 2025. "An analytic framework for assessing the impacts of physical risk through a (climate-related) expected shortfall," International Review of Economics & Finance, Elsevier, vol. 103(C).
  • Handle: RePEc:eee:reveco:v:103:y:2025:i:c:s1059056025005209
    DOI: 10.1016/j.iref.2025.104357
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    1. Brik, Hatem, 2024. "Climate risk and financial stability: Assessing non-performing loans in Chinese banks," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 17(3), pages 303-315, June.
    2. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    3. Helena Redondo & Elisa Aracil, 2024. "Climate‐related credit risk: Rethinking the credit risk framework," Global Policy, London School of Economics and Political Science, vol. 15(S1), pages 21-33, March.
    4. Romain Svartzman & Patrick Bolton & Morgan Despres & Luiz Awazu Pereira Da Silva & Frédéric Samama, 2021. "Central banks, financial stability and policy coordination in the age of climate uncertainty: a three-layered analytical and operational framework," Climate Policy, Taylor & Francis Journals, vol. 21(4), pages 563-580, April.
    5. Julia Anna Bingler & Chiara Colesanti Senni, 2022. "Taming the Green Swan: a criteria-based analysis to improve the understanding of climate-related financial risk assessment tools," Climate Policy, Taylor & Francis Journals, vol. 22(3), pages 356-370, March.
    6. Kumar, Nikhil & Poonia, Vikas & Gupta, B.B. & Goyal, Manish Kumar, 2021. "A novel framework for risk assessment and resilience of critical infrastructure towards climate change," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    7. Bua, Giovanna & Kapp, Daniel & Ramella, Federico & Rognone, Lavinia, 2022. "Transition versus physical climate risk pricing in European financial markets: a text-based approach," Working Paper Series 2677, European Central Bank.
    8. A. M. Vicedo-Cabrera & N. Scovronick & F. Sera & D. Royé & R. Schneider & A. Tobias & C. Astrom & Y. Guo & Y. Honda & D. M. Hondula & R. Abrutzky & S. Tong & M. de Sousa Zanotti Stagliorio Coelho & P., 2021. "The burden of heat-related mortality attributable to recent human-induced climate change," Nature Climate Change, Nature, vol. 11(6), pages 492-500, June.
    9. Nicola Ann Ranger & Olivier Mahul & Irene Monasterolo, 2022. "Assessing Financial Risks from Physical Climate Shocks," World Bank Publications - Reports 37041, The World Bank Group.
    10. L. Kourouma & Denis Dupré & O. Taramasco & G. Sanfilippo, 2011. "Extreme Value at Risk and Expected Shortfall during Financial Crisis," Post-Print halshs-00650913, HAL.
    11. Faruk Ülgen, 2024. "Greening Finance? What Institutional Options for a Sustainable Transition?," Journal of Economic Issues, Taylor & Francis Journals, vol. 58(2), pages 642-649, April.
    12. Glenn D. Rudebusch, 2021. "Climate Change Is a Source of Financial Risk," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2021(03), pages 01-06, February.
    13. Al Mamun, Md & Boubaker, Sabri & Nguyen, Duc Khuong, 2022. "Green finance and decarbonization: Evidence from around the world," Finance Research Letters, Elsevier, vol. 46(PB).
    14. repec:oap:ijaefa:v:17:y:2023:i:2:p:469-482:id:1203 is not listed on IDEAS
    15. Giacomo Bressan & Anja Đuranović & Irene Monasterolo & Stefano Battiston, 2024. "Asset-level assessment of climate physical risk matters for adaptation finance," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    16. Penikas, Henry & Vasilyeva, Ekaterina, 2024. "Redefining the degree of industry greenness using input–output tables," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1073-1090.
    17. Fan, Wenna & Wang, Feng & Zhang, Hao & Yan, Bin & Ling, Rui & Jiang, Hongfei, 2024. "Is climate change fueling commercial banks’ non-performing loan ratio? Empirical evidence from 31 provinces in China," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    18. Jianzhou Wang & Shuai Wang & Mengzheng Lv & He Jiang, 2024. "Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    19. Stefano BATTISTON, 2019. "The importance of being forward-looking: managing financial stability in the face of climate risk," Financial Stability Review, Banque de France, issue 23, pages 39-48, June.
    20. Miriam Breitenstein & Duc Khuong Nguyen & Thomas Walther, 2021. "Environmental Hazards And Risk Management In The Financial Sector: A Systematic Literature Review," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 512-538, April.
    21. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    22. L. Kourouma & Denis Dupré & G. Sanfilippo & O. Taramasco, 2011. "Extreme Value at Risk and Expected Shortfall during Financial Crisis," Post-Print halshs-00658495, HAL.
    23. D. Coumou & G. Di Capua & S. Vavrus & L. Wang & S. Wang, 2018. "The influence of Arctic amplification on mid-latitude summer circulation," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    24. Nick Taylor, 2023. "‘Making financial sense of the future’: actuaries and the management of climate-related financial risk," New Political Economy, Taylor & Francis Journals, vol. 28(1), pages 57-75, January.
    25. Pal Peter Kolozsi & Sandor Ladanyi & Andras Straubinger, 2022. "Measuring the Climate Risk Exposure of Financial Assets - Methodological Challenges and Central Bank Practices," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 21(1), pages 113-140.
    26. Curcio, Domenico & Gianfrancesco, Igor & Vioto, Davide, 2023. "Climate change and financial systemic risk: Evidence from US banks and insurers," Journal of Financial Stability, Elsevier, vol. 66(C).
    27. Mete Feridun & Hasan Güngör, 2020. "Climate-Related Prudential Risks in the Banking Sector: A Review of the Emerging Regulatory and Supervisory Practices," Sustainability, MDPI, vol. 12(13), pages 1-20, July.
    28. William D. Nordhaus & Andrew Moffat, 2017. "A Survey of Global Impacts of Climate Change: Replication, Survey Methods, and a Statistical Analysis," NBER Working Papers 23646, National Bureau of Economic Research, Inc.
    29. Martin L. Weitzman, 2014. "Fat Tails and the Social Cost of Carbon," American Economic Review, American Economic Association, vol. 104(5), pages 544-546, May.
    30. Lagoarde-Ségot, Thomas & Revelli, Christophe, 2023. "Ecological money and finance. Introducing ecological risk-free assets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    31. William Nordhaus, 2018. "Evolution of modeling of the economics of global warming: changes in the DICE model, 1992–2017," Climatic Change, Springer, vol. 148(4), pages 623-640, June.
    32. Christian Traeger, 2014. "A 4-Stated DICE: Quantitatively Addressing Uncertainty Effects in Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 59(1), pages 1-37, September.
    33. Carè, R. & Fatima, R. & Boitan, I.A., 2024. "Central banks and climate risks: Where we are and where we are going?," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 1200-1229.
    34. Nan Zhou & José Luis Vilar‐Zanón & Jose Garrido & Antonio José Heras‐Martínez, 2024. "Measuring climate change from an actuarial perspective: A survey of insurance applications," Global Policy, London School of Economics and Political Science, vol. 15(S7), pages 34-46, November.
    35. Md Al Mamun & Sabri Boubaker & Ahmet Sensoy, 2022. "Green Finance and Decarbonization: Evidence from around the World," Post-Print hal-05337462, HAL.
    36. Alexander Braun & Sebastian Utz & Jiahua Xu, 2019. "Are insurance balance sheets carbon-neutral? Harnessing asset pricing for climate change policy†," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 44(4), pages 549-568, October.
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    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • G0 - Financial Economics - - General
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General

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