An efficient shared socioeconomic pathways-based methodology for assessing climate risks of a large credit portfolio

We examine climate-related exposure within a large credit portfolio, addressing transition and physical risks. We design a modeling methodology that begins with the Shared Socioeconomic Pathways (SSP) scenarios and ends with describing the losses of a portfolio of obligors. The SSP scenarios impact the physical risk of each obligor via a DICE-inspired damage function and their transition risk through production, requiring optimal adjustment. To achieve optimal production, the obligor optimizes various energy sources to align its greenhouse gas (GHG) emission trajectories with SSP objectives, while accounting for uncertainties in consumption trajectories. Ultimately, we obtain a Gaussian factor model whose dimension is of the order of the number of obligors. Two efficient dimension reduction methods (Polynomial Chaos Expansion and Principal Component Analysis) provide a fast and accurate method for analyzing credit portfolio losses.