Systematic Effects among Loss Given Defaults and their Implications on Downturn Estimation

Abstract Banks are obliged to provide downturn estimates for loss given defaults (LGDs) in the internal ratings-based approach. While downturn conditions are characterized by systematically higher LGDs, it is unclear which factors may best capture these conditions. As LGDs depend on recovery payments which are collected during varying economic conditions in the resolution process, it is challenging to identify suitable economic variables. Using a Bayesian Finite Mixture Model, we adapt random effects to measure economic conditions and to generate downturn estimates. We find that systematic effects vary among regions, i.e., the US and Europe, and strongly deviate from the economic cycle. Our approach offers straightforward supportive tools for decision makers. Risk managers are enabled to select their individual margin of conservatism based on their portfolios, while regulators might set a lower bound to guarantee conservatism. In comparison to other approaches, our proposal appears to be conservative enough during downturn conditions and inhibits over-conservatism.