Modeling credit risk in the presence of central bank and government intervention

Bernd Engelmann

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Estimating macroeconomic models became challenging in the last two years because interventions by governments and central banks as a response to the Covid-19 outbreak have distorted long-term relationships between default rates and macroeconomic variables such as unemployment and GDP growth.
Predictions of macroeconomic models have been highly inaccurate in 2020 and 2021 and model inaccuracy cannot be fixed within the framework of time series modeling.
A possible solution to this problem is including government intervention explicitly in a credit risk model by utilizing the one-factor credit risk model underlying the Basel risk weight function.
The additional factor for government intervention has to be determined from recent default observations and expert opinion on the future intensity of 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 had 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, such as monthly payments to citizens, which allow borrowers to service their debt despite suffering income loss due to unemployment or business closures. This results in substantially lower default rates than those predicted by credit models. Using data published by the US Federal Reserve Bank in 2021 Q3, this paper suggests a framework that quantifies the effect of central bank and government interventions and shows how to include intervention scenarios in credit models, improving the accuracy of their short-term predictions and allowing analysts to evaluate long-term scenarios. In addition, potential side effects of intervention, such as increased inflation, are quantified.

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