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What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors?

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  • Hyeongwoo Kim
  • Jisoo Son

Abstract

Charge-offs signal critical information regarding the risk level of loan portfolios in the banking system, and they indicate the potential for systemic risk towards deep recessions. Utilizing consolidated financial statements, we have compiled the net charge-off rate (COR) data from the 10 largest U.S. bank holding companies (BHCs) for disaggregated loans, including business loans, real estate loans, and consumer loans, as well as the average top 10 COR for each loan category. We propose factor-augmented forecasting models for CORs that incorporate latent common factor estimates, including targeted factors, via an array of data dimensionality reduction methods for a large panel of macroeconomic predictors. Our models have demonstrated superior performance compared with benchmark forecasting models especially well for business loan and real estate loan CORs, while predicting consumer loan CORs remains challenging especially at short horizons. Notably, real activity factors improve the out-of-sample predictability over the benchmarks for business loan CORs even when financial sector factors are excluded.

Suggested Citation

  • Hyeongwoo Kim & Jisoo Son, 2024. "What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors?," Auburn Economics Working Paper Series auwp2024-01, Department of Economics, Auburn University.
    • Handle: RePEc:abn:wpaper:auwp2024-01
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    Cited by:

    1. Carlos Canizares Martinez, 2023. "Leaning against housing booms fueled by credit," Working and Discussion Papers WP 9/2023, Research Department, National Bank of Slovakia.
    2. Sarthak Behera & Hyeongwoo Kim & Soohyon Kim, 2025. "Asymmetric Roles of Macroeconomic Variables in the Real Exchange Rate: Insights from U.S.-Korea Data," Auburn Economics Working Paper Series auwp2025-01, Department of Economics, Auburn University.

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    More about this item

    Keywords

    Net Charge-Off Rate; Top 10 Bank Holding Companies; Disaggregated Loan CORs; Principal Component Analysis; Partial Least Squares; Out-of-Sample Forecast;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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