IDEAS home Printed from https://ideas.repec.org/p/ajf/louvlr/2019007.html

Recovery rates: Uncertainty certainly matters

Author

Listed:
  • Gambetti, Paolo
  • Gauthier, Geneviève
  • Vrins, Frédéric

Abstract

Previous studies identify default rate as the main systematic determinant of bond recovery rates. We revisit this paradigm by investigating the impact of another factor, economic uncertainty. Based on a wide sample of American default issues and relying on beta regression models, well-suited for the bounded, heteroskedastic and skewed sample of recovery rates, we analyze the determinants of recovery rate distributions. We find economic uncertainty to be of paramount importance, as it proves to be the most important systematic determinant of recovery rate distributions, significant for both their mean and dispersion. By contrast, default rate remains a key determinant of the dispersion of these distributions, but not for their means. Considering this evidence is critical to the sound implementation of stochastic recovery rate models used by financial institutions for the computation of regulatory capital.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Gambetti, Paolo & Gauthier, Geneviève & Vrins, Frédéric, 2019. "Recovery rates: Uncertainty certainly matters," LIDAM Reprints LFIN 2019007, Université catholique de Louvain, Louvain Finance (LFIN).
    • Handle: RePEc:ajf:louvlr:2019007
    Note: In : Journal of Banking & Finance, Vol. 106, no. 9, p. 371-383 (2019)
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Barbagli, Matteo & Vrins, Frédéric, 2023. "Accounting for PD-LGD dependency: A tractable extension to the Basel ASRF framework," Economic Modelling, Elsevier, vol. 125(C).
    2. Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
    3. Bertrand Candelon & Francesco Roccazzella, 2025. "Evaluating Inflation Forecasts in the Euro Area and the Role of the ECB," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(3), pages 978-1008, April.
    4. Li, Yong & Mu, Yuandong & Qin, Tianyu, 2021. "Economic uncertainty: A key factor to understanding idiosyncratic volatility puzzle," Finance Research Letters, Elsevier, vol. 42(C).
    5. Jennifer Betz & Ralf Kellner & Daniel Rösch, 2021. "Time matters: How default resolution times impact final loss rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 619-644, June.
    6. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    7. Roccazzella, Francesco & Candelon, Bertrand, 2022. "Should we care about ECB inflation expectations?," LIDAM Discussion Papers LFIN 2022004, Université catholique de Louvain, Louvain Finance (LFIN).
    8. Distaso, Walter & Roccazzella, Francesco & Vrins, Frédéric, 2025. "Business cycle and realized losses in the consumer credit industry," European Journal of Operational Research, Elsevier, vol. 323(3), pages 1024-1039.
    9. Pascal François, 2019. "The Determinants of Market-Implied Recovery Rates," Risks, MDPI, vol. 7(2), pages 1-15, May.
    10. Paolo Gambetti & Francesco Roccazzella & Frédéric Vrins, 2022. "Meta-Learning Approaches for Recovery Rate Prediction," Risks, MDPI, vol. 10(6), pages 1-29, June.
    11. Specht, Leon, 2023. "An Empirical Analysis of European Credit Default Swap Spread Dynamics," Junior Management Science (JUMS), Junior Management Science e. V., vol. 8(1), pages 1-42.
    12. Barbagli, Matteo & François, Pascal & Gauthier, Geneviève & Vrins, Frédéric, 2025. "The role of CDS spreads in explaining bond recovery rates," Journal of Banking & Finance, Elsevier, vol. 174(C).
    13. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    14. Sopitpongstorn, Nithi & Silvapulle, Param & Gao, Jiti & Fenech, Jean-Pierre, 2021. "Local logit regression for loan recovery rate," Journal of Banking & Finance, Elsevier, vol. 126(C).
    15. Bhanot, Karan & François, Pascal & Kadapakkam, Palani-Rajan, 2025. "How does the structure of an interest expense cap change the tax benefits of debt?," Journal of Corporate Finance, Elsevier, vol. 91(C).
    16. Liu, Haibo & Tang, Qihe, 2025. "Modeling and pricing credit risk with a focus on recovery risk," Journal of Banking & Finance, Elsevier, vol. 170(C).
    17. Nazemi, Abdolreza & Baumann, Friedrich & Fabozzi, Frank J., 2022. "Intertemporal defaulted bond recoveries prediction via machine learning," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1162-1177.
    18. Masahiko Egami & Rusudan Kevkhishvili, 2020. "Loss-Given-Default Modeling by Post-Last Passage Time Process," Papers 2009.00868, arXiv.org, revised Nov 2025.
    19. Meng, Qingbin & Huang, Haozheng & Li, Xinyu & Wang, Song, 2023. "Short-selling and corporate default risk: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 398-417.
    20. Hui-Ching Chuang & Jau-er Chen, 2023. "Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles," Econometrics, MDPI, vol. 11(1), pages 1-20, February.
    21. Stephan Höcht & Aleksey Min & Jakub Wieczorek & Rudi Zagst, 2022. "Explaining Aggregated Recovery Rates," Risks, MDPI, vol. 10(1), pages 1-30, January.
    22. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).

    More about this item

    JEL classification:

    • 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
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ajf:louvlr:2019007. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Séverine De Visscher (email available below). General contact details of provider: https://edirc.repec.org/data/lfuclbe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.