Credit Risk Modeling in the Presence of Central Bank and Government Intervention

Abstract

Since the outbreak of Covid-19 and the central bank and government interventions that followed, new challenges in credit modeling have emerged. Relations between credit risk and macroeconomic drivers that have been fairly stable over decades have broken down. An example is the unemployment rate which has been widely used in predicting default rates in retail loan segments. Since mid-2020 this no longer works because of government interventions like monthly payments to citizens which allows them to service their debt despite suffering income loss due to unemployment or business closures. This results in substantially lower default rates than predicted by credit models. In this article, using data published by the US Federal Reserve Bank in Q1 2021, a framework is suggested that quantifies the effect of central bank and government interventions and shows how to include intervention scenarios into credit models improving the accuracy of their short-term predictions and allowing analysts to evaluate long-term scenarios. Furthermore, potential side-effects of intervention like increased inflation are quantified.

Keywords: Credit Risk, Default Probability, Covid-19, Basel II, IFRS 9, Stress Testing

JEL Classification: G21