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The initial Climate-Extended Risk Model (CERM) addresses the estimate ofclimate-related financial risk embedded within a bank loan portfolio, through aclimatic extension of the Basel II IRB model. It uses a Gaussian copula modelcalibrated with non stationary macro-correlations in order to reflect thefuture evolution of climate-related financial risks. In this complementaryarticle, we propose a stochastic forward-looking methodology to calibrateclimate macro-correlation evolution from scientific climate data, for physicaland transition efforts specifically. We assume a global physical and transitionrisk, likened to persistent greenhouse gas (GHG) concentration in theatmosphere. The economic risk is considered stationary and can therefore becalibrated with a backward-looking methodology. We present 4 key principles tomodel the GDP and we propose to model the economic, physical and transitioneffort factors with three interdependent stochastic processes allowing for acalibration with seven well defined parameters. These parameters can becalibrated using public data. This new approach means not only to evaluateclimate risks without picking any specific scenario but also allows to fill thegap between current one year approach of regulatory and economic capital modelsand the necessarily long-term view of climate risks by designing a framework toevaluate the resulting credit loss on each step (typically yearly) of thetransition path. This new approach could prove instrumental in the 2022 contextof central banks weighing the pros and cons of a climate capital charge.

A Stochastic Climate Model -- An approach to calibrate the Climate-Extended Risk Model (CERM)