Creditor recovery: the macroeconomic dependence of industry equilibrium
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- Mora, Nada, 2015. "Creditor recovery: The macroeconomic dependence of industry equilibrium," Journal of Financial Stability, Elsevier, vol. 18(C), pages 172-186.
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- Gambetti, Paolo & Roccazzella, Francesco & Vrins, Frédéric, 2022. "Meta-Learning Approaches for Recovery Rate Prediction," LIDAM Reprints LFIN 2022011, Université catholique de Louvain, Louvain Finance (LFIN).
- 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,"
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- 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.
- 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," Papers 1804.07022, arXiv.org.
- Barbagli, Matteo & François, Pascal & Gauthier, Geneviève & Vrins, Frédéric, 2024. "The role of CDS spreads in explaining bond recovery rates," LIDAM Discussion Papers LFIN 2024002, Université catholique de Louvain, Louvain Finance (LFIN).
- 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.
- Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
- 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.
- Nazemi, Abdolreza & Fabozzi, Frank J., 2018. "Macroeconomic variable selection for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 14-25.
- 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.
- 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.
- 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.
- Frame, W. Scott & Lazaryan, Nika & McLemore, Ping & Mihov, Atanas, 2024. "Operational loss recoveries and the macroeconomic environment: Evidence from the U.S. banking sector," Journal of Banking & Finance, Elsevier, vol. 165(C).
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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
NEP fields
This paper has been announced in the following NEP Reports:- NEP-MAC-2013-08-05 (Macroeconomics)
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