Permanent layoff and consumer credit card loss forecasting

Zilong Liu, Hongyan Liang

Research output: Contribution to journalArticlepeer-review


Purpose: The unemployment rate (UR) is the leading macroeconomic indicator used in the credit card loss forecasting. COVID-19 pandemic has caused an unprecedented level of volatility in the labor market variables, leading to new challenges to use UR in the credit risk modeling framework. This paper examines the dynamic relationship between the credit card charge-off rate and the unemployment rate over time. Design/methodology/approach: This study uses quarterly observations of charge-off rates on credit card loans of all commercial banks from Q1 1990 to Q4 2020. Univariate, multivariable, machine learning, and regime-switching time series modeling are employed in this research. Findings: The authors decompose UR into two components – temporary and permanent UR. The authors find the spike in UR during COVID-19 is mainly attributed to the surge in temporary layoffs. More importantly, the authors find that the credit card charge-off rate is primarily driven by permanent UR while temporary UR has little predictive power. During recessions, permanent UR seems to be a stronger indicator than total UR. This research highlights the importance of using permanent UR for credit risk modeling. Originality/value: The findings in the research can be applied to the credit card loss forecasting and CECL reserve models. In addition, this research also has implications for banks, macroeconomic data vendors, regulators, and policymakers.

Original languageEnglish (US)
Pages (from-to)789-807
Number of pages19
JournalManagerial Finance
Issue number5
StatePublished - Apr 17 2023


  • COVID-19
  • Credit risk
  • Loss forecasting
  • Macroeconomics indicators
  • Unemployment rate

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Finance


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