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Loss given default models incorporating macroeconomic variables for credit cards

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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. Petr Polak & Jiri Panos, 2019. "The Impact of Expectations on IFRS 9 Loan Loss Provisions," Research and Policy Notes 2019/03, Czech National Bank, Research and Statistics Department.
  3. Gürtler, Marc & Hibbeln, Martin, 2013. "Improvements in loss given default forecasts for bank loans," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2354-2366.
  4. Walter Distaso & Wolfgang Lefever & Angelo Luisi & Francesco Roccazzella, 2025. "The Impact of Wildfires on Loss Given Default: Evidence from Defaulted Consumer Credits," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 25/1129, Ghent University, Faculty of Economics and Business Administration.
  5. Kyriakos Georgiou & Athanasios N. Yannacopoulos, 2023. "Probability of Default modelling with L\'evy-driven Ornstein-Uhlenbeck processes and applications in credit risk under the IFRS 9," Papers 2309.12384, arXiv.org.
  6. Salvatore D. Tomarchio & Antonio Punzo, 2019. "Modelling the loss given default distribution via a family of zero‐and‐one inflated mixture models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1247-1266, October.
  7. Chen, Xiaowei & Wang, Gang & Zhang, Xiangting, 2019. "Modeling recovery rate for leveraged loans," Economic Modelling, Elsevier, vol. 81(C), pages 231-241.
  8. Gourieroux, Christian & Lu, Yang, 2019. "Least impulse response estimator for stress test exercises," Journal of Banking & Finance, Elsevier, vol. 103(C), pages 62-77.
  9. Lozinskaia Agata & Ozhegov Evgeniy, 2016. "Key Determinants of Demand, Credit Underwriting, and Performance on Government-Insured Mortgage Loans in Russia," EERC Working Paper Series 16/03e, EERC Research Network, Russia and CIS.
  10. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2015. "Support vector regression for loss given default modelling," European Journal of Operational Research, Elsevier, vol. 240(2), pages 528-538.
  11. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Enhancing two-stage modelling methodology for loss given default with support vector machines," European Journal of Operational Research, Elsevier, vol. 263(2), pages 679-689.
  12. 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.
  13. 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.
  14. Robert J. Powell & Duc H. Vo, 2020. "A Comprehensive Stability Indicator for Banks," Risks, MDPI, vol. 8(1), pages 1-15, February.
  15. Yuta Tanoue & Satoshi Yamashita & Hideaki Nagahata, 2020. "Comparison study of two-step LGD estimation model with probability machines," Risk Management, Palgrave Macmillan, vol. 22(3), pages 155-177, September.
  16. 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.
  17. 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.
  18. 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.
  19. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2021. "Determinants of corporate exposure at default under distressed economic and financial conditions in a developing economy: the case of Zimbabwe," Risk Management, Palgrave Macmillan, vol. 23(1), pages 123-149, June.
  20. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
  21. Matuszyk, Anna & So, Mee Chi & Mues, Christophe & Moore, Angela, 2016. "Modelling repayment patterns in the collections process for unsecured consumer debt: A case studyAuthor-Name: Thomas, Lyn C," European Journal of Operational Research, Elsevier, vol. 249(2), pages 476-486.
  22. Natalia Nehrebecka, 2019. "Bank loans recovery rate in commercial banks: A case study of non-financial corporations," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(1), pages 139-172.
  23. Jaime Leyva & Tiago Pinheiro, 2025. "Recovery, quantified: How we built a dataset of loan recovery estimates," Working Papers w202523, Banco de Portugal, Economics and Research Department.
  24. 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.
  25. Thamayanthi Chellathurai, 2017. "Probability Density Of Recovery Rate Given Default Of A Firm’S Debt And Its Constituent Tranches," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-34, June.
  26. Rubén García-Céspedes & Manuel Moreno, 2020. "Random LGD adjustments in the Vasicek credit risk model," The European Journal of Finance, Taylor & Francis Journals, vol. 26(18), pages 1856-1875, December.
  27. Lukasz Prorokowski, 2018. "IFRS 9 in credit risk modelling Evidence from SLOOS for Poland," Bank i Kredyt, Narodowy Bank Polski, vol. 49(6), pages 639-670.
  28. Cheng, Dan & Cirillo, Pasquale, 2018. "A reinforced urn process modeling of recovery rates and recovery times," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 1-17.
  29. Jérémy Leymarie & Christophe Hurlin & Antoine Patin, 2018. "Loss Functions for LGD Models Comparison," Post-Print hal-01923050, HAL.
  30. Raffaella Calabrese, 2012. "Estimating bank loans loss given default by generalized additive models," Working Papers 201224, Geary Institute, University College Dublin.
  31. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
  32. 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.
  33. Kim, Hyeongjun & Cho, Hoon & Ryu, Doojin, 2018. "An empirical study on credit card loan delinquency," Economic Systems, Elsevier, vol. 42(3), pages 437-449.
  34. Hurlin, Christophe & Leymarie, Jérémy & Patin, Antoine, 2018. "Loss functions for Loss Given Default model comparison," European Journal of Operational Research, Elsevier, vol. 268(1), pages 348-360.
  35. Tomas Konecny & Jakub Seidler & Aelta Belyaeva & Konstantin Belyaev, 2017. "The Time Dimension of the Links Between Loss Given Default and the Macroeconomy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 67(6), pages 462-491, October.
  36. Calabrese, Raffaella, 2014. "Downturn Loss Given Default: Mixture distribution estimation," European Journal of Operational Research, Elsevier, vol. 237(1), pages 271-277.
  37. Do, Hung Xuan & Rösch, Daniel & Scheule, Harald, 2018. "Predicting loss severities for residential mortgage loans: A three-step selection approach," European Journal of Operational Research, Elsevier, vol. 270(1), pages 246-259.
  38. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
  39. 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.
  40. Yurchenko, Yurii, 2019. "The impact of macroeconomic factors on collateral value within the framework of expected credit loss calculation," MPRA Paper 97135, University Library of Munich, Germany.
  41. Yashkir, Olga & Yashkir, Yuriy, 2013. "Loss Given Default Modelling: Comparative Analysis," MPRA Paper 46147, University Library of Munich, Germany.
  42. Giuseppe Orlando & Roberta Pelosi, 2020. "Non-Performing Loans for Italian Companies: When Time Matters. An Empirical Research on Estimating Probability to Default and Loss Given Default," IJFS, MDPI, vol. 8(4), pages 1-22, November.
  43. Hussain, Inayat & Durand, Robert B. & Harris, Mark N., 2016. "Default resolution and access to fresh credit in an emerging market," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 256-274.
  44. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
  45. Emily Johnston Ross & Lynn Shibut, 2021. "Loss Given Default, Loan Seasoning and Financial Fragility: Evidence from Commercial Real Estate Loans at Failed Banks," The Journal of Real Estate Finance and Economics, Springer, vol. 63(4), pages 630-661, November.
  46. Starosta, Wojciech, 2021. "Loss given default decomposition using mixture distributions of in-default events," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1187-1199.
  47. Leow, Mindy & Crook, Jonathan, 2014. "Intensity models and transition probabilities for credit card loan delinquencies," European Journal of Operational Research, Elsevier, vol. 236(2), pages 685-694.
  48. Partha Sengupta & Christopher H. Wheeler, 2024. "Credit card loss forecasting: Some lessons from COVID," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2448-2477, November.
  49. Hui Ye & Anthony Bellotti, 2019. "Modelling Recovery Rates for Non-Performing Loans," Risks, MDPI, vol. 7(1), pages 1-17, February.
  50. Hon, Pak Shun & Bellotti, Tony, 2016. "Models and forecasts of credit card balance," European Journal of Operational Research, Elsevier, vol. 249(2), pages 498-505.
  51. Sajjad Taghiyeh & David C Lengacher & Robert B Handfield, 2020. "Loss Rate Forecasting Framework Based on Macroeconomic Changes: Application to US Credit Card Industry," Papers 2006.07911, arXiv.org.
  52. 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.
  53. Chrysovalantis Gaganis & Panagiota Papadimitri & Fotios Pasiouras & Menelaos Tasiou, 2023. "Social traits and credit card default: a two-stage prediction framework," Annals of Operations Research, Springer, vol. 325(2), pages 1231-1253, June.
  54. Djeundje, Viani Biatat & Crook, Jonathan, 2018. "Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards," European Journal of Operational Research, Elsevier, vol. 271(2), pages 697-709.
  55. Cheng, Hui & Jiang, Cuiqing & Wang, Zhao & Ni, Xiaoya, 2025. "Multi-view locally weighted regression for loss given default forecasting," International Journal of Forecasting, Elsevier, vol. 41(1), pages 290-306.
  56. Ho, Kung-Cheng & Lee, Shih-Cheng & Chen, Jiun-Lin, 2022. "Book-to-market equity and asset correlations—An international study," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 258-274.
  57. Johannes Kriebel & Kevin Yam, 2020. "Forecasting recoveries in debt collection: Debt collectors and information production," European Financial Management, European Financial Management Association, vol. 26(3), pages 537-559, June.
  58. Abdelkader Derbali & Slaheddine Hallara, 2016. "Measuring systemic risk of Greek banks: New approach by using the epidemic model “SEIR”," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1153864-115, December.
  59. Ying Wu & Garvit Arora & Xuan Mei, 2024. "Using CPI in Loss Given Default Forecasting Models for Commercial Real Estate Portfolio," Papers 2402.15498, arXiv.org.
  60. 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.
  61. 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.
  62. Miller, Patrick & Töws, Eugen, 2018. "Loss given default adjusted workout processes for leases," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 189-201.
  63. Peter-Hendrik Ingermann & Frederik Hesse & Christian Bélorgey & Andreas Pfingsten, 2016. "The recovery rate for retail and commercial customers in Germany: a look at collateral and its adjusted market values," Business Research, Springer;German Academic Association for Business Research, vol. 9(2), pages 179-228, August.
  64. Hartmann-Wendels, Thomas & Miller, Patrick & Töws, Eugen, 2014. "Loss given default for leasing: Parametric and nonparametric estimations," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 364-375.
  65. Tong, Edward N.C. & Mues, Christophe & Brown, Iain & Thomas, Lyn C., 2016. "Exposure at default models with and without the credit conversion factor," European Journal of Operational Research, Elsevier, vol. 252(3), pages 910-920.
  66. Tanoue, Yuta & Kawada, Akihiro & Yamashita, Satoshi, 2017. "Forecasting loss given default of bank loans with multi-stage model," International Journal of Forecasting, Elsevier, vol. 33(2), pages 513-522.
  67. Allen, D.E. & Powell, R.J. & Singh, A.K., 2016. "Take it to the limit: Innovative CVaR applications to extreme credit risk measurement," European Journal of Operational Research, Elsevier, vol. 249(2), pages 465-475.
  68. 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).
  69. Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi, 2023. "Modelling credit card exposure at default using vine copula quantile regression," European Journal of Operational Research, Elsevier, vol. 311(1), pages 387-399.
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