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The determinants of bank loan recovery rates in good times and bad – New evidence

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

Abstract

We find that factors explaining bank loan recovery rates differ depending on the state of an underlying credit cycle. Our modelling approach incorporates a two-state Markov switching mechanism to capture underlying economic conditions. This latent credit cycle variable helps 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-global financial crisis (GFC) period, the paper develops and implements a dynamic model for bank loan recovery rates. We accommodate the distinctive empirical features of the recovery rate data, while incorporating a large number of possible determinants. We find that certain loan-specific and other variables hold different explanatory power with respect to recovery rates in ‘good’ versus ‘bad’ times in the credit cycle, i.e. depending on underlying credit market conditions. Our findings demonstrate the importance of accounting for counter-cyclical expected recovery rates when determining capital retention levels.

Suggested Citation

  • Wang, Hong & Forbes, Catherine S. & Fenech, Jean-Pierre & Vaz, John, 2020. "The determinants of bank loan recovery rates in good times and bad – New evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 875-897.
    • Handle: RePEc:eee:jeborg:v:177:y:2020:i:c:p:875-897
    DOI: 10.1016/j.jebo.2020.06.001
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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; Counter-cyclical; Bayesian estimation; LASSO prior; Markov switching;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • 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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