The determinants of bank loan recovery rates in good times and bad -- new evidence

Hong Wang (), Catherine Forbes, Jean-Pierre Fenech () and John Vaz ()

No 7/18, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We find that factors explaining bank loan recovery rates vary depending on the state of the economic cycle. Our modeling approach incorporates a two-state Markov switching mechanism as a proxy for the latent credit cycle, helping to explain differences in observed recovery rates over time. Using US bank default loan data from Moody's Ultimate Recovery Database and covering the pre- and post-GFC period, this paper develops a dynamic predictive model for bank loan recovery rates, accommodating the distinctive empirical features of the recovery rate data while incorporating a large number of possible determinants. We find that the probability of default and certain loan-specific and other variables hold different explanatory power with respect to recovery rates over `good' and `bad' times in the credit cycle, meaning that the relationship between recovery rates and certain loan characteristics, firm characteristics and the probability of default differs depending on underlying credit market conditions. Our findings demonstrate the importance of accounting for countercyclical expected recovery rates when determining capital retention levels.

Keywords: credit risk; Basel III; countercyclicality; Bayesian estimation; LASSO prior; Markov switching. (search for similar items in EconPapers)
JEL-codes: G17 G21 G28 (search for similar items in EconPapers)
Pages: 29
Date: 2018
New Economics Papers: this item is included in nep-ban and nep-rmg
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: The determinants of bank loan recovery rates in good times and bad – New evidence (2020)
Working Paper: The determinants of bank loan recovery rates in good times and bad - new evidence (2018)
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