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Macroeconomic variable selection for creditor recovery rates

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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. Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
  3. Gambetti, Paolo & Gauthier, Geneviève & Vrins, Frédéric, 2019. "Recovery rates: Uncertainty certainly matters," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 371-383.
  4. Alan Tidwell & Yan (Olivia) Lu & Junsoo Lee & Piyali Banerjee, 2023. "Nature of comovements in US state and MSA housing prices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(4), pages 959-989, July.
  5. 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.
  6. Elyasiani, Elyas & Movaghari, Hadi, 2022. "Determinants of corporate cash holdings: An application of a robust variable selection technique," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 967-993.
  7. Jean‐François Bégin & Mathieu Boudreault & Mathieu Thériault, 2024. "Leveraging prices from credit and equity option markets for portfolio risk management," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(1), pages 122-147, January.
  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. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
  10. Pascal François, 2019. "The Determinants of Market-Implied Recovery Rates," Risks, MDPI, vol. 7(2), pages 1-15, May.
  11. Paolo Gambetti & Francesco Roccazzella & Frédéric Vrins, 2022. "Meta-Learning Approaches for Recovery Rate Prediction," Risks, MDPI, vol. 10(6), pages 1-29, June.
  12. Volha Audzei & Sergey Slobodyan, 2025. "Dynamic Sparse Adaptive Learning," Working Papers 2025/9, Czech National Bank, Research and Statistics Department.
  13. Volha Audzei & Sergey Slobodyan, 2024. "Dynamic Sparse Restricted Perceptions Equilibria," CERGE-EI Working Papers wp792, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
  14. Li, Qianwen & Qian, Tingyu & Wang, Hui & Sun, Chuanwang, 2025. "Exploring the value of green: The impact factors on China's second-hand green housing prices based on geographically weighted Lasso regressions," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
  15. 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.
  16. Sohrabi, Narges & Movaghari, Hadi, 2020. "Reliable factors of Capital structure: Stability selection approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 296-310.
  17. 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).
  18. Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
  19. Jochen Güntner & Benjamin Karner, 2023. "The bond agio premium," Economics working papers 2023-13, Department of Economics, Johannes Kepler University Linz, Austria.
  20. 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).
  21. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
  22. Maria Carannante & Valeria D’Amato & Paola Fersini & Salvatore Forte & Giuseppe Melisi, 2024. "Machine learning due diligence evaluation to increase NPLs profitability transactions on secondary market," Review of Managerial Science, Springer, vol. 18(7), pages 1963-1983, July.
  23. Lucey, Brian & Urquhart, Andrew & Zhang, Hanxiong, 2022. "UK Vice Chancellor compensation: Do they get what they deserve?," The British Accounting Review, Elsevier, vol. 54(4).
  24. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
  25. Xing, Kai & Luo, Dan & Liu, Lanlan, 2023. "Macroeconomic conditions, corporate default, and default clustering," Economic Modelling, Elsevier, vol. 118(C).
  26. 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.
  27. 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.
  28. Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.
  29. 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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