Quantifying the Impact of Physical Climate Risk on Corporate Probability of Default A Dynamic Credit Risk Model for Bank Loan Portfolios
物理的気候リスクが企業のデフォルト確率に与える影響の定量化：銀行ローンポートフォリオのための動的信用リスクモデル (AI 翻訳)
Olorato Rantaba
🤖 gxceed AI 要約
日本語
本論文は、物理的気候リスクを企業価値の確率的プロセスに組み込んだ動的信用リスクモデルを提案する。気候感応度パラメータを用いて、気候ショックがデフォルト確率を系統的に上昇させることを示す。無視すれば長期信用リスクを過小評価し、銀行ポートフォリオのシステミックリスクを増幅する。モンテカルロシミュレーションで実証する。
English
This paper develops a dynamic credit risk model incorporating physical climate risk into firm value dynamics. It shows that climate risk systematically increases default probabilities and that ignoring it leads to underestimation of long-term credit risk. The model captures how climate risk acts as a common factor amplifying systemic risk in bank loan portfolios. Monte Carlo simulations illustrate the effects.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の金融庁は気候変動ストレステストを導入しており、本モデルは銀行のポートフォリオ評価や自己資本規制に活用できる。特に日本は自然災害リスクが高いため、物理的気候リスクの定量化が重要となる。
In the global GX context
This model provides a transparent framework for climate stress testing, aligning with NGFS and Basel Committee guidelines. It helps incorporate physical climate risk into credit risk assessment, crucial for financial institutions globally as regulators demand more robust climate scenario analyses.
👥 読者別の含意
🔬研究者:A foundation for extending credit risk models with climate factors, including transition risk and sectoral differentiation.
🏢実務担当者:Banks can use the model to enhance internal credit risk assessments and stress testing frameworks.
🏛政策担当者:Regulators should consider this approach for incorporating physical climate risk into prudential supervision and capital requirements.
📄 Abstract（原文）
Physical climate risk has emerged as a critical source of financial instability, yet its integration into traditional credit risk models remains limited. Existing frameworks, particularly structural credit risk models, typically assume stable economic conditions and fail to account for persistent and cumulative environmental shocks. This creates a significant gap in the accurate estimation of corporate default probabilities, especially over long-term horizons. This study develops a dynamic credit risk model that explicitly incorporates physical climate risk into firm value dynamics. The model is formulated in discrete time, where firm value evolves as a stochastic process influenced by both financial shocks and a persistent climate risk factor. By introducing a climate sensitivity parameter, the framework captures the direct impact of environmental conditions on firm performance and creditworthiness. Analytical results demonstrate that climate risk systematically increases the probability of default. In particular, higher exposure to climate risk and greater persistence of climate shocks both lead to elevated default probabilities. The model also shows that ignoring climate risk results in consistent underestimation of long-term credit risk, with important implications for financial stability. Numerical simulations based on Monte Carlo methods illustrate how climate risk affects firm value trajectories and portfolio-level default behavior. The results highlight the role of climate risk as a common factor that induces correlation across firms, thereby amplifying systemic risk in bank loan portfolios. The proposed framework provides a transparent and mathematically tractable approach for incorporating physical climate risk into credit risk assessment. It offers practical relevance for banks and regulators seeking to enhance climate stress testing and improve long-term risk measurement in the financial system.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.2139/ssrn.6665098first seen 2026-06-29 08:12:13
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