Climate aware lending allocation under NGFS scenarios - A Monte Carlo approach

Marina Palaisti

Papers from arXiv.org

Abstract: This paper develops a framework to assess loan portfolio budget in credit portfolios exposed to climate risk over a 3-5 year horizon. Using short-term scenarios from the NGFS, the approach translates transition and physical risk pathways into changes in obligor default risk. Firm performance is modeled within a structural credit risk framework where scenario-dependent climate shocks adjust the drift of firm value dynamics. Defaults occur when firm value falls below a liability threshold. Joint defaults are simulated using Monte Carlo methods with dependence captured via principal components. Resulting default-probability estimates inform a constrained optimization used to examine lending allocation across exposures. Climate scenario choice affects model-implied default probabilities and the resulting risk-return tradeoff across obligors and sectors. The results show scenario-specific differences in default-probability distributions, upper-tail risk, and lending allocation patterns. Under the benchmark synthetic portfolio, HWTP produces the highest mean and upper-tail PDs, while SWUC produces the lowest mean and median PDs. These rankings are model-implied and calibration-dependent rather than empirical rankings of climate-scenario severity. The framework remains transparent and can be refined as more granular climate and firm-level data become available. The framework helps banks link NGFS scenarios to sector-level credit risk and portfolio loss outcomes. It can support ICAAP and climate stress testing by highlighting exposures most sensitive to climate risk, adding a forward-looking overlay to existing models.

Date: 2026-09
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