  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.
