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Creditor recovery: the macroeconomic dependence of industry equilibrium

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  • Nada Mora

Abstract

This paper reconciles industry conditions with the state of the economy in driving asset liquidation values and, therefore, recovery rates on defaulted debt securities. Macroeconomic effects matter but they operate differentially at the industry level.

Suggested Citation

  • Nada Mora, 2013. "Creditor recovery: the macroeconomic dependence of industry equilibrium," Research Working Paper RWP 13-06, Federal Reserve Bank of Kansas City.
    • Handle: RePEc:fip:fedkrw:rwp13-06
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    File URL: https://www.kansascityfed.org/documents/7709/rwp13-06.pdf
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    Cited by:

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    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. 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.
    4. John, Kose & Mateti, Ravi S. & Vasudevan, Gopala & Amira, Khaled, 2016. "Investor protection and firm value: Evidence from PIPE offerings," Journal of Financial Stability, Elsevier, vol. 26(C), pages 78-89.
    5. 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.
    6. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond," Journal of Financial Stability, Elsevier, vol. 28(C), pages 1-15.
    7. Nazemi, Abdolreza & Fabozzi, Frank J., 2018. "Macroeconomic variable selection for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 14-25.
    8. Marc Gürtler & Marvin Zöllner, 2023. "Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 251-287, March.
    9. Juan Pineiro-Chousa & Marcos Vizcaíno-González & Samuel Ribeiro-Navarrete, 2019. "Using voting decisions to identify shocks in the financial services industry," Service Business, Springer;Pan-Pacific Business Association, vol. 13(2), pages 419-431, June.

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    More about this item

    Keywords

    Credit; Risk; Business cycles;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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