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Ultimate recovery mixtures

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Cited by:

  1. Nazemi, Abdolreza & Rezazadeh, Hani & Fabozzi, Frank J. & Höchstötter, Markus, 2022. "Deep learning for modeling the collection rate for third-party buyers," International Journal of Forecasting, Elsevier, vol. 38(1), pages 240-252.
  2. Chen, Xiaowei & Wang, Gang & Zhang, Xiangting, 2019. "Modeling recovery rate for leveraged loans," Economic Modelling, Elsevier, vol. 81(C), pages 231-241.
  3. Nazemi, Abdolreza & Fatemi Pour, Farnoosh & Heidenreich, Konstantin & Fabozzi, Frank J., 2017. "Fuzzy decision fusion approach for loss-given-default modeling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 780-791.
  4. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
  5. Gambetti, Paolo & Gauthier, Geneviève & Vrins, Frédéric, 2019. "Recovery rates: Uncertainty certainly matters," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 371-383.
  6. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2018. "Systematic Effects among Loss Given Defaults and their Implications on Downturn Estimation," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1113-1144.
  7. Pascal François, 2019. "The Determinants of Market-Implied Recovery Rates," Risks, MDPI, vol. 7(2), pages 1-15, May.
  8. Paolo Gambetti & Francesco Roccazzella & Frédéric Vrins, 2022. "Meta-Learning Approaches for Recovery Rate Prediction," Risks, MDPI, vol. 10(6), pages 1-29, June.
  9. 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.
  10. 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).
  11. Jobst, Rainer & Kellner, Ralf & Rösch, Daniel, 2020. "Bayesian loss given default estimation for European sovereign bonds," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1073-1091.
  12. Chih-Kang Chu & Ruey-Ching Hwang, 2019. "Predicting Loss Distributions for Small-Size Defaulted-Debt Portfolios Using a Convolution Technique that Allows Probability Masses to Occur at Boundary Points," Journal of Financial Services Research, Springer;Western Finance Association, vol. 56(1), pages 95-117, August.
  13. Miller, Patrick & Töws, Eugen, 2018. "Loss given default adjusted workout processes for leases," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 189-201.
  14. Hwang, Ruey-Ching & Chu, Chih-Kang & Yu, Kaizhi, 2020. "Predicting LGD distributions with mixed continuous and discrete ordinal outcomes," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1003-1022.
  15. Jennifer Betz & Maximilian Nagl & Daniel Rösch, 2022. "Credit line exposure at default modelling using Bayesian mixed effect quantile regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2035-2072, October.
  16. Jochen Güntner & Benjamin Karner, 2023. "The bond agio premium," Economics working papers 2023-13, Department of Economics, Johannes Kepler University Linz, Austria.
  17. Jankowitsch, Rainer & Nagler, Florian & Subrahmanyam, Marti G., 2014. "The determinants of recovery rates in the US corporate bond market," Journal of Financial Economics, Elsevier, vol. 114(1), pages 155-177.
  18. Krüger, Steffen & Rösch, Daniel, 2017. "Downturn LGD modeling using quantile regression," Journal of Banking & Finance, Elsevier, vol. 79(C), pages 42-56.
  19. Ruey-Ching Hwang & Huimin Chung & C. K. Chu, 2016. "A Two-Stage Probit Model for Predicting Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 50(3), pages 311-339, December.
  20. 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.
  21. Ruey-Ching Hwang & Chih-Kang Chu & Kaizhi Yu, 2021. "Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(3), pages 143-172, June.
  22. Nithi Sopitpongstorn & Param Silvapulle & Jiti Gao, 2017. "Local logit regression for recovery rate," Monash Econometrics and Business Statistics Working Papers 19/17, Monash University, Department of Econometrics and Business Statistics.
  23. González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2018. "Proposal on ELBE and LGD in-default: tackling capital requirements after the financial crisis," Working Paper Series 2165, European Central Bank.
  24. 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).
  25. Nazemi, Abdolreza & Heidenreich, Konstantin & Fabozzi, Frank J., 2018. "Improving corporate bond recovery rate prediction using multi-factor support vector regressions," European Journal of Operational Research, Elsevier, vol. 271(2), pages 664-675.
  26. 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.
  27. 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.
  28. Krüger, Steffen & Rösch, Daniel & Scheule, Harald, 2018. "The impact of loan loss provisioning on bank capital requirements," Journal of Financial Stability, Elsevier, vol. 36(C), pages 114-129.
  29. Mili, Medhi & Sahut, Jean-Michel & Teulon, Frédéric, 2018. "Modeling recovery rates of corporate defaulted bonds in developed and developing countries," Emerging Markets Review, Elsevier, vol. 36(C), pages 28-44.
  30. Marta Ramos González & Antonio Partal Ureña & Pilar Gómez Fernández-Aguado, 2021. "Regulatory Estimates for Defaulted Exposures: A Case Study of Spanish Mortgages," Mathematics, MDPI, vol. 9(9), pages 1-9, April.
  31. 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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