Forecasting Credit Card Portfolio Losses in the Great Recession: A Study in Model Risk

Abstract

Credit card portfolios represent a significant component of the balance sheets of the largest US banks. The charge‐off rate in this asset class increased drastically during the Great Recession. The recent economic downturn offers a unique opportunity to analyze the performance of credit risk models applied to credit card portfolios under conditions of economic stress. Specifically, we evaluate three potential sources of model risk: model specification, sample selection, and stress scenario selection. Our analysis indicates that model specifications that incorporate interactions between policy variables and core account characteristics generate the most accurate loss projections across risk segments. Models estimated over a time frame that includes a significant economic downturn are able to project levels of credit loss consistent with those experienced during the Great Recession. Models estimated over a time frame that does not include a significant economic downturn can severely under-predict credit loss in some cases, and the level of forecast error can be significantly impacted by model specification assumptions. Higher credit-score segments of the portfolio are proportionally more severely impacted by downturn economic conditions and model specification assumptions. The selection of the stress scenario can have a dramatic impact on projected loss.