Spatial Stress Testing and Climate Value-at-Risk: A Quantitative Framework for ICAAP and Pillar 2
空間ストレステストと気候バリューアットリスク：ICAAPおよびピラー2のための定量的フレームワーク (AI 翻訳)
F. Rania
🤖 gxceed AI 要約
日本語
本論文は、空間的に明示的なジャンプ拡散資産損失モデルとプルデンシャルに整合したリスク指標を組み合わせた気候金融リスク測定の定量的フレームワークを開発する。物理的危険と移行変数を結合し、テールリスクと地理的異質性を捉える。SCVaRやCESなどの指標を算出し、銀行貸出ポートフォリオや年金ポートフォリオ、ソブリンエクスポージャーへの応用を示す。ICAAPおよびピラー2における気候調整資本バッファーや開示に活用可能。
English
This paper develops a quantitative framework for climate–financial risk measurement combining a spatially explicit jump–diffusion asset–loss model with prudential risk metrics. It yields portfolio-level Spatial Climate Value-at-Risk (SCVaR) and Conditional Expected Shortfall (CES) across scenarios, validated statistically. Applications to bank, pension, and sovereign portfolios show region- and sector-dependent tail losses. Outputs support ICAAP/Pillar 2: climate-adjusted capital buffers and disclosure bridges.
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
日本の金融機関にとって、ICAAPおよびピラー2への気候リスク組み入れは喫緊の課題であり、本フレームワークは定量的な気候リスク評価手法を提供する。特に、GAR（グリーンアセット比率）と補完的な開示指標を提案しており、SSBJ基準や金融庁の気候関連リスク管理ガイダンスに整合する可能性がある。
In the global GX context
Globally, this framework addresses the supervisory expectation for climate stress testing under Basel III and ISSB standards. By providing statistically validated, forward-looking measures like SCVaR and CES, it bridges the gap between qualitative disclosures and risk-sensitive capital requirements, aligning with NGFS scenarios and TCFD recommendations.
👥 読者別の含意
🔬研究者:Provides a validated, spatially explicit risk measurement method combining diffusion and jump processes for climate scenarios, with clear statistical robustness tests.
🏢実務担当者:Enables banks and asset managers to quantify climate-adjusted capital buffers and scenario-based stress losses for ICAAP/Pillar 2 and disclosure (GAR bridge).
🏛政策担当者:Offers a decision-useful framework for setting climate-adjusted capital requirements and evaluating systemic tail risks from physical and transition shocks.
📄 Abstract（原文）
This paper develops a quantitative framework for climate–financial risk measurement that combines a spatially explicit jump–diffusion asset–loss model with prudentially aligned risk metrics. The approach connects regional physical hazards and transition variables derived from climate-consistent pathways to asset returns and credit parameters through the use of climate-adjusted volatilities and jump intensities. Fat tails and geographic heterogeneity are captured by it, which conventional diffusion-based or purely narrative stress tests fail to reflect. The framework delivers portfolio-level Spatial Climate Value-at-Risk (SCVaR) and Expected Shortfall (ES) across scenario–horizon matrices and incorporates an explicit robustness layer (block bootstrap confidence intervals, unconditional/conditional coverage backtests, and structural-stability tests). All ES measures are understood as Conditional Expected Shortfall (CES), i.e., tail expectations evaluated conditional on climate stress scenarios. Applications to bank loan books, pension portfolios, and sovereign exposures show how climate shocks reprice assets, alter default and recovery dynamics, and amplify tail losses in a region- and sector-dependent manner. The resulting, statistically validated outputs are designed to be decision-useful for Internal Capital Adequacy Assessment Process (ICAAP) and Pillar 2: climate-adjusted capital buffers, scenario-based stress calibration, and disclosure bridges that complement alignment metrics such as the Green Asset Ratio (GAR). Overall, the framework operationalises a move from exposure tallies to forward-looking, risk-sensitive, and auditable measures suitable for supervisory dialogue and internal risk appetite.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/jrfm19010048first seen 2026-06-29 08:30:36
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