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

Hyeongwoo Kim () and Jisoo Son

No auwp2023-06, Auburn Economics Working Paper Series from Department of Economics, Auburn University

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 categoy. 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.

Keywords: Net Charge-Off Rate; Top 10 Bank Holding Companies; Disaggregated Loan CORs; Principal Component Analysis; Partial Least Squares; Out-of-Sample Forecast (search for similar items in EconPapers)
JEL-codes: C38 C53 C55 G01 G17 (search for similar items in EconPapers)
Date: 2023-07
New Economics Papers: this item is included in nep-ban and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: What charge-off rates are predictable by macroeconomic latent factors? (2024)
Working Paper: What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors? (2024)
Working Paper: What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors? (2023)
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