学术文献库

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