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

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  • Hong Wang
  • Catherine S. Forbes
  • Jean-Pierre Fenech
  • John Vaz

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.

Suggested Citation

  • Hong Wang & Catherine S. Forbes & Jean-Pierre Fenech & John Vaz, 2018. "The determinants of bank loan recovery rates in good times and bad -- new evidence," Monash Econometrics and Business Statistics Working Papers 7/18, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2018-7
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    Cited by:

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    2. Fakhrul Wahab & Majid Jamal Khan & Muhammad Yar Khan & Rukhshanda Mushtaq, 2024. "The impact of climate change on agricultural productivity and agricultural loan recovery; evidence from a developing economy," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(10), pages 24777-24790, October.
    3. Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
    4. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Corporate Loan Recovery Rates under Downturn Conditions in a Developing Economy: Evidence from Zimbabwe," Risks, MDPI, vol. 10(10), pages 1-24, October.
    5. Maria Patricia Durango‐Gutiérrez & Juan Lara‐Rubio & Andrés Navarro‐Galera, 2023. "Analysis of default risk in microfinance institutions under the Basel III framework," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1261-1278, April.
    6. Aleksey Min & Matthias Scherer & Amelie Schischke & Rudi Zagst, 2020. "Modeling Recovery Rates of Small- and Medium-Sized Entities in the US," Mathematics, MDPI, vol. 8(11), pages 1-18, October.

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    More about this item

    Keywords

    credit risk; Basel III; countercyclicality; Bayesian estimation; LASSO prior; Markov switching.;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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