Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data

Citations

Cited by:

1. Chen, Xiaowei & Wang, Gang & Zhang, Xiangting, 2019. "Modeling recovery rate for leveraged loans," Economic Modelling, Elsevier, vol. 81(C), pages 231-241.
2. 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.
3. Hao, Siyuan, 2023. "Modeling hospitalization medical expenditure of the elderly in China," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 450-461.
4. Agata M. Lozinskaia & Evgeniy M. Ozhegov & Alexander M. Karminsky, 2016. "Discontinuity in Relative Credit Losses: Evidence from Defaults on Government-Insured Residential Mortgages," HSE Working papers WP BRP 55/FE/2016, National Research University Higher School of Economics.
5. 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.
6. Shi, Baofeng & Zhao, Xue & Wu, Bi & Dong, Yizhe, 2019. "Credit rating and microfinance lending decisions based on loss given default (LGD)," Finance Research Letters, Elsevier, vol. 30(C), pages 124-129.
7. 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).
8. 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.
   • Abdelkader Derbali & Slaheddine Hallara & David Mcmillan, 2016. "Measuring systemic risk of Greek banks: New approach by using the epidemic model “SEIR”," Post-Print hal-01696006, HAL.
9. Miller, Patrick & Töws, Eugen, 2018. "Loss given default adjusted workout processes for leases," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 189-201.
10. 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.
11. 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.
12. 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.
13. 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.
14. Konstantin Gorgen & Abdolreza Nazemi & Melanie Schienle, 2022. "Robust Knockoffs for Controlling False Discoveries With an Application to Bond Recovery Rates," Papers 2206.06026, arXiv.org.
15. 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.
16. Janette Larney & James Samuel Allison & Gerrit Lodewicus Grobler & Marius Smuts, 2023. "Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
17. Been, Vicki & Weselcouch, Mary & Voicu, Ioan & Murff, Scott, 2013. "Determinants of the incidence of U.S. Mortgage Loan Modifications," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3951-3973.
18. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
19. Michela Pelizza & Klaus R. Schenk-Hoppé, 2020. "Pricing Defaulted Italian Mortgages," JRFM, MDPI, vol. 13(2), pages 1-14, February.
20. Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi, 2023. "A mixture model for credit card exposure at default using the GAMLSS framework," International Journal of Forecasting, Elsevier, vol. 39(1), pages 503-518.
21. 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.
22. 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.
23. 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.
24. 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.
25. Michael C. S. Wong & Ho Ming Ho, 2023. "A Framework for Integrating Extreme Weather Risk, Probability of Default, and Loss Given Default for Residential Mortgage Loans," Sustainability, MDPI, vol. 15(15), pages 1-16, August.
26. 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.