🤖 AI Summary
This study addresses the quantification of the impact of different climate scenarios on expected credit losses (ECL) for financial assets. To this end, it proposes an operational framework for measuring scenario-induced impacts by leveraging existing provisioning systems within financial institutions. The approach adjusts probabilities of default to reflect climate-related shocks and integrates mappings of risk drivers with standardized exposure grouping methodologies, thereby enabling comparable scenario analyses across institutions. The framework provides both a theoretical foundation and a practical implementation pathway for regulators conducting standardized climate stress tests. It has been successfully applied in the 2024 joint climate scenario analysis conducted by the Office of the Superintendent of Financial Institutions Canada and the Autorité des marchés financiers du Québec.
📝 Abstract
In this paper, we present a methodology for measuring the impact of scenarios on the expected losses of exposures by leveraging the existing provisioning infrastructure within financial institutions, where scenario effects are captured through changes in probabilities of default. We then describe how to design and implement a scenario test where risk drivers are given for standardized groupings of exposures, and the groupings are defined based on common features of the exposures. The methodology presented served as a theoretical foundation for the standardized climate scenario exercise conducted in 2024 by the Office of the Superintendent of Financial Institutions of Canada and Quebec's Autorite des Marches Financiers.