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Modelling Robust Lending Decisions under Climate Scenario Ambiguity: A Minimax-Regret Framework with NGFS Short-Term Scenarios

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  • Marina Palaisti

Abstract

This paper develops a public-data framework for evaluating incremental bank lending when plausible climate scenarios imply different sector credit outcomes but defensible scenario probabilities are unavailable. The calibration combines 2025 Shared National Credit industry commitments, January 2026 U.S. industry leverage, interest coverage and equity volatility from Damodaran, and official 2027 sector-level probability-of-default adjustments from the NGFS CLIMACRED model. One-year Merton probabilities provide independent baseline credit-risk proxies for 16 non-financial sectors representing \$5.2808 trillion of commitments. Minimax regret is compared with symmetric scenario weighting and maximin under two principal decision scores: expected loss and a credit-compensation payoff using Damodaran synthetic spreads. DAPS raises the commitment-weighted one-year PD from 0.0592\% to 0.0897\%. At a 12.5\% sector cap, minimax regret reduces maximum expected-loss regret by 29.1\% relative to symmetric scenario weighting; under the credit-compensation score the reduction is 69.1\%. Sensitivity to concentration limits, crosswalk weights, Merton inputs and the climate-PD transport shows that the value of minimax regret is conditional on scenario-specific sector rankings differing near binding portfolio constraints. The contribution is an operational decision-fragility framework for financial institutions and supervisors, not evidence about realized bank behavior or universally optimal climate lending.

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  • Marina Palaisti, 2026. "Modelling Robust Lending Decisions under Climate Scenario Ambiguity: A Minimax-Regret Framework with NGFS Short-Term Scenarios," Papers 2610.00104, arXiv.org.
    • Handle: RePEc:arx:papers:2610.00104
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