Predicting the cure of a defaulted company: Nonlinear relationships between loan-related variables and the cure probability
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DOI: 10.1016/j.ribaf.2024.102395
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- Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Identifying hidden patterns in credit risk survival data using Generalised Additive Models," European Journal of Operational Research, Elsevier, vol. 277(1), pages 366-376.
- Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 903-911, May.
- Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
- Yang, Qi & He, Haijin & Lu, Bin & Song, Xinyuan, 2022. "Mixture additive hazards cure model with latent variables: Application to corporate default data," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
- Daniel Berg, 2007. "Bankruptcy prediction by generalized additive models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(2), pages 129-143, March.
- Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
- Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.
- Nailong Zhang & Qingyu Yang & Aidan Kelleher & Wujun Si, 2019. "A new mixture cure model under competing risks to score online consumer loans," Quantitative Finance, Taylor & Francis Journals, vol. 19(7), pages 1243-1253, July.
- Ruey-Ching Hwang & K. F. Cheng & Jack C. Lee, 2007. "A semiparametric method for predicting bankruptcy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 317-342.
- Daniel Porath, 2006. "Estimating probabilities of default for German savings banks and credit cooperatives," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 58(3), pages 214-233, July.
- Dirick, Lore & Claeskens, Gerda & Baesens, Bart, 2015. "An Akaike information criterion for multiple event mixture cure models," European Journal of Operational Research, Elsevier, vol. 241(2), pages 449-457.
- Rada Dakovic & Claudia Czado & Daniel Berg, 2010. "Bankruptcy prediction in Norway: a comparison study," Applied Economics Letters, Taylor & Francis Journals, vol. 17(17), pages 1739-1746.
- Renault, Olivier & Scaillet, Olivier, 2004.
"On the way to recovery: A nonparametric bias free estimation of recovery rate densities,"
Journal of Banking & Finance, Elsevier, vol. 28(12), pages 2915-2931, December.
- Olivier RENAULT & Olivier SCAILLET, 2003. "On the Way to Recovery: A Nonparametric Bias Free Estimation of Recovery Rate Densities," FAME Research Paper Series rp83, International Center for Financial Asset Management and Engineering.
- Yildiray Yildirim, 2008. "Estimating Default Probabilities of CMBS Loans with Clustering and Heavy Censoring," The Journal of Real Estate Finance and Economics, Springer, vol. 37(2), pages 93-111, August.
- K. F. Cheng & C. K. Chu & Ruey-Ching Hwang, 2010. "Predicting bankruptcy using the discrete-time semiparametric hazard model," Quantitative Finance, Taylor & Francis Journals, vol. 10(9), pages 1055-1066.
- Lev, Baruch & Sunder, Shyam, 1979. "Methodological issues in the use of financial ratios," Journal of Accounting and Economics, Elsevier, vol. 1(3), pages 187-210, December.
- Christian Lohmann & Thorsten Ohliger, 2017. "Nonlinear Relationships and Their Effect on the Bankruptcy Prediction," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 18(3), pages 261-287, August.
- Wolter, Marcus & Rösch, Daniel, 2014. "Cure events in default prediction," European Journal of Operational Research, Elsevier, vol. 238(3), pages 846-857.
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More about this item
Keywords
Company cure; Cure probability; Global Credit Data; Loss given default;All these keywords.
JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
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