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Recovery rates: Uncertainty certainly matters

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  • 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.

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  • Gambetti, Paolo & Gauthier, Geneviève & Vrins, Frédéric, 2019. "Recovery rates: Uncertainty certainly matters," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 371-383.
    • Handle: RePEc:eee:jbfina:v:106:y:2019:i:c:p:371-383
    DOI: 10.1016/j.jbankfin.2019.07.010
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    Cited by:

    1. Pascal François, 2019. "The Determinants of Market-Implied Recovery Rates," Risks, MDPI, vol. 7(2), pages 1-15, May.
    2. Paolo Gambetti & Francesco Roccazzella & Frédéric Vrins, 2022. "Meta-Learning Approaches for Recovery Rate Prediction," Risks, MDPI, vol. 10(6), pages 1-29, June.
    3. 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).
    4. 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.
    5. Roccazzella, Francesco & Candelon, Bertrand, 2022. "Should we care about ECB inflation expectations?," LIDAM Discussion Papers LFIN 2022004, Université catholique de Louvain, Louvain Finance (LFIN).
    6. 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.
    7. Stephan Höcht & Aleksey Min & Jakub Wieczorek & Rudi Zagst, 2022. "Explaining Aggregated Recovery Rates," Risks, MDPI, vol. 10(1), pages 1-30, January.
    8. 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).
    9. 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.
    10. Barbagli, Matteo & Vrins, Frédéric, 2023. "Accounting for PD-LGD dependency: A tractable extension to the Basel ASRF framework," Economic Modelling, Elsevier, vol. 125(C).
    11. Li, Yong & Mu, Yuandong & Qin, Tianyu, 2021. "Economic uncertainty: A key factor to understanding idiosyncratic volatility puzzle," Finance Research Letters, Elsevier, vol. 42(C).
    12. 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.
    13. Masahiko Egami & Rusudan Kevkhishvili, 2020. "Post-Last Exit Time Process and its Application to Loss-Given-Default Distribution," Papers 2009.00868, arXiv.org, revised Mar 2024.
    14. 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.
    15. 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).

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    More about this item

    Keywords

    Recovery rate; Loss given default; Corporate bond; Credit risk; Uncertainty;
    All these keywords.

    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

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