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

Hyeongwoo Kim () and Jisoo Son

MPRA Paper from University Library of Munich, Germany

Abstract: Charge-offs signal important information about the riskiness of loan portfolios in the banking system, which can generate systemic risk towards deep recessions. We compiled the net charge-off rate (COR) data of the top 10 bank holding companies (BHCs) in the U.S., utilizing consolidated financial statements. We propose factor-augmented forecasting models for CORs by estimating latent common factors, including targeted factors, via an array of data dimensionality reduction methods for a large panel of macroeconomic predictors. Our models outperform the benchmark models especially well for business loan and real estate loan CORs, while enhancing predictive contents for consumer loan CORs is difficult especially at short horizons. 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; Bank Holding Companies; Principal Component Analysis; Partial Least Squares; Out-of-Sample Forecast (search for similar items in EconPapers)
JEL-codes: C5 F3 (search for similar items in EconPapers)
Date: 2023-03-31
New Economics Papers: this item is included in nep-ban
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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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:116880

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