CLIMATE RISK AND ASSET PRICING: INTEGRATING ENVIRONMENTAL FACTORS INTO FINANCIAL MODELS

Nurcahya Hartaty Posumah

doi:10.62207/6y8dgx80

Authors

Nurcahya Hartaty Posumah Universitas Muhammadiyah Luwuk, Central Sulawesi, Indonesia Author

DOI:

https://doi.org/10.62207/6y8dgx80

Keywords:

Climate Risk, Asset Pricing, ESG, Climate Beta, Financial Models, Machine Learning.

Abstract

Climate change has become a significant systemic risk in the global financial landscape, yet traditional asset pricing models such as the Capital Asset Pricing Model (CAPM) and the Fama-French Three-Factor Model have not explicitly integrated this risk. A report by the BlackRock Investment Institute (2020) suggests a 5–10% valuation reduction for companies with high climate risk exposure, while the Swiss Re Institute (2021) projects an economic impact of up to 18% of global GDP by 2050. This gap highlights the urgent need to revise asset pricing models to reflect the reality of climate risk. This study aims to synthesize approaches to integrating climate risk (physical and transition) into asset pricing models, identify methodological challenges in its measurement and representation, and evaluate the validity of climate risk metrics such as ESG scores and climate beta. Using a narrative review approach, this study analyzes literature from Scopus, Web of Science, JSTOR, and ScienceDirect databases (2010–2025). The search strategy involved keywords such as “climate risk”, “asset pricing”, “ESG”, and “machine learning”. Thematic analysis was applied to identify patterns in theoretical approaches, climate variables, adopted models, and empirical results. Findings suggest that climate risk integration can be achieved through the Climate Beta concept, ESG-based models, portfolio sorting based on carbon exposure, and hybrid models with machine learning. Empirical evidence suggests that carbon-intensive sectors experience lower stock returns and increased volatility, although methodological inconsistencies remain a challenge. Integrating climate risk into traditional asset pricing models, particularly through the development of adaptive multifactor models, has the potential to improve return prediction and risk assessment. More robust methodologies, standardized environmental data, and multidisciplinary collaboration are needed to produce valid and applicable models.

References

Ardia, D., Bluteau, K., Boudt, K., & Inghelbrecht, K. (2023). Climate change concerns and the performance of green vs. brown stocks. Management Science, 69(12), 7607-7632. https://doi.org/10.1287/mnsc.2022.4636

Aziz, S., Dowling, M., Hammami, H., & Piepenbrink, A. (2021). Machine learning in finance: a topic modeling approach. European Financial Management, 28(3), 744-770. https://doi.org/10.1111/eufm.12326

Bacciu, V., Hatzaki, M., Karali, A., Cauchy, A., Giannakopoulos, C., Spano, D., … & Briche, É. (2021). Investigating the climate-related risk of forest fires for mediterranean islands’ blue economy. Sustainability, 13(18), 10004. https://doi.org/10.3390/su131810004

Balcılar, M., Gabauer, D., Gupta, R., & Pierdzioch, C. (2023). Climate risks and forecasting stock market returns in advanced economies over a century. Mathematics, 11(9), 2077. https://doi.org/10.3390/math11092077

BlackRock Investment Institute. (2020). Sustainability: The tectonic shift transforming investing. BlackRock. Retrieved from https://www.blackrock.com/corporate/literature/whitepaper/bii-sustainability-future-investing.pdf

Campbell, J. (2014). Empirical asset pricing: eugene fama, lars peter hansen, and robert shiller. Scandinavian Journal of Economics, 116(3), 593-634. https://doi.org/10.1111/sjoe.12070

Chandra, A. and Thenmozhi, M. (2017). Behavioural asset pricing: review and synthesis. Journal of Interdisciplinary Economics, 29(1), 1-31. https://doi.org/10.1177/0260107916670559

Chiah, M., Chai, D., Zhong, A., & Li, S. (2016). A better model? an empirical investigation of the fama–french five‐factor model in australia. International Review of Finance, 16(4), 595-638. https://doi.org/10.1111/irfi.12099

Drobetz, W. and Otto, T. (2021). Empirical asset pricing via machine learning: evidence from the european stock market. Journal of Asset Management, 22(7), 507-538. https://doi.org/10.1057/s41260-021-00237-x

D’Orazio, P. and Popoyan, L. (2018). Fostering green investments and tackling climate-related financial risks: which role for macroprudential policies?. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3106350

Feridun, M. and Güngör, H. (2020). Climate-related prudential risks in the banking sector: a review of the emerging regulatory and supervisory practices. Sustainability, 12(13), 5325. https://doi.org/10.3390/su12135325

Foye, J., Mramor, D., & Pahor, M. (2013). A respecified fama french three‐factor model for the new european union member states. Journal of International Financial Management and Accounting, 24(1), 3-25. https://doi.org/10.1111/jifm.12005

Giglio, S., Kelly, B., & Stroebel, J. (2021). Climate finance. Annual Review of Financial Economics, 13(1), 15-36. https://doi.org/10.1146/annurev-financial-102620-103311

Götze, T., Gürtler, M., & Witowski, E. (2023). Forecasting accuracy of machine learning and linear regression: evidence from the secondary cat bond market. Journal of Business Economics, 93(9), 1629-1660. https://doi.org/10.1007/s11573-023-01138-8

Ju, Y. (2024). Climate change effects in stock markets. Highlights in Business Economics and Management, 41, 370-375. https://doi.org/10.54097/430kvj67

Krueger, P., Sautner, Z., & Starks, L. (2020). The importance of climate risks for institutional investors. Review of Financial Studies, 33(3), 1067-1111. https://doi.org/10.1093/rfs/hhz137

Lasisi, L., Omoke, P., & Salisu, A. (2024). Climate policy uncertainty and stock market volatility. Asian Economics Letters, 5(2). https://doi.org/10.46557/001c.37246

Liao, Z., Lin, Z., & Quan, X. (2023). Analysis of the evolution of the multiple factors financing model. Highlights in Business Economics and Management, 19, 450-458. https://doi.org/10.54097/hbem.v19i.11981

Louche, C., Busch, T., Crifo, P., & Marcus, A. (2019). Financial markets and the transition to a low-carbon economy: challenging the dominant logics. Organization & Environment, 32(1), 3-17. https://doi.org/10.1177/1086026619831516

Ma, F., Cao, J., Wang, Y., Vigne, S., & Dong, D. (2023). Dissecting climate change risk and financial market instability: implications for ecological risk management. Risk Analysis, 45(3), 496-522. https://doi.org/10.1111/risa.14265

Monasterolo, I., Battiston, S., Janetos, A., & Zheng, Z. (2017). Vulnerable yet relevant: the two dimensions of climate-related financial disclosure. Climatic Change, 145(3-4), 495-507. https://doi.org/10.1007/s10584-017-2095-9

MSCI ESG Research. (2023). 2023 ESG trends to watch. MSCI Inc. Retrieved from https://www.msci.com/documents/1296102/72389102/MSCI-2023-ESG-Trends.pdf

Network for Greening the Financial System (NGFS). (2022). Climate-related risks to financial stability. NGFS. Retrieved from https://www.ngfs.net/sites/default/files/medias/documents/climate_risks_to_financial_stability.pdf

Neumann, J. and Strzepek, K. (2014). State of the literature on the economic impacts of climate change in the united states. Journal of Benefit-Cost Analysis, 5(03), 411-443. https://doi.org/10.1515/jbca-2014-9003

Ospina-Holguín, J. and Ospina, A. (2021). The search for time-series predictability-based anomalies. Journal of Business Economics and Management, 23(1), 1-19. https://doi.org/10.3846/jbem.2021.15650

Pankratz, N., Bauer, R., & Derwall, J. (2023). Climate change, firm performance, and investor surprises. Management Science, 69(12), 7352-7398. https://doi.org/10.1287/mnsc.2023.4685

Pástor, Ľ., Stambaugh, R., & Taylor, L. (2021). Sustainable investing in equilibrium. Journal of Financial Economics, 142(2), 550-571. https://doi.org/10.1016/j.jfineco.2020.12.011

Romo, J. (2011). Pricing digital outperformance options with uncertain correlation. International Journal of Theoretical and Applied Finance, 14(05), 709-722. https://doi.org/10.1142/s0219024911006425

Swiss Re Institute. (2021). The economics of climate change: No action not an option. Swiss Re. Retrieved from https://www.swissre.com/institute/research/topics-and-risk-dialogues/climate-and-natural-catastrophe-risk/expertise-publication-economics-of-climate-change.html

Thistlethwaite, J. and Wood, M. (2018). Insurance and climate change risk management: rescaling to look beyond the horizon. British Journal of Management, 29(2), 279-298. https://doi.org/10.1111/1467-8551.12302

Thomä, J. and Chenet, H. (2016). Transition risks and market failure: a theoretical discourse on why financial models and economic agents may misprice risk related to the transition to a low-carbon economy. Journal of Sustainable Finance & Investment, 7(1), 82-98. https://doi.org/10.1080/20430795.2016.1204847

Zhang, S. (2024). Harnessding the power of machine learning to superchange capm forecasts. Advances in Economics Management and Political Sciences, 110(1), 114-121. https://doi.org/10.54254/2754-1169/110/2024ed0099

Zhang, X., Zhang, M., & Fang, Z. (2023). Impact of climate risk on the financial performance and financial policies of enterprises. Sustainability, 15(20), 14833. https://doi.org/10.3390/su152014833

Zhao, F. (2023). A literature study of asset pricing based on machine learning method. Advances in Economics Management and Political Sciences, 34(1), 27-36. https://doi.org/10.54254/2754-1169/34/20231669