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Forecasting Retail Portfolio Credit Risk

Author

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  • DANIEL RÖSCH
  • HARALD SCHEULE

Abstract

A major topic in retail lending is the measurement of the inherent portfolio credit risk. The needs for a better understanding and dealing with default risky securities have been reinforced by the Basel Committee on Banking Supervision [1999a, 1999b, 2000, 2001a, 2001b, 2002, 2003] which has proposed a revision of the standards for banks' capital requirements.
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Suggested Citation

  • Daniel Rösch & Harald Scheule, 2004. "Forecasting Retail Portfolio Credit Risk," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 5(2), pages 16-32, February.
    • Handle: RePEc:eme:jrfpps:eb022983
    DOI: 10.1108/eb022983
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    Citations

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    Cited by:

    1. Muteba Mwamba, John Weirstrass & Mhlophe, Bongani, 2019. "Modelling Asset Correlations of Revolving Loan Defaults in South Africa," MPRA Paper 97340, University Library of Munich, Germany.
    2. Lee, Yongwoong & Poon, Ser-Huang, 2014. "Forecasting and decomposition of portfolio credit risk using macroeconomic and frailty factors," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 69-92.
    3. Hamerle, Alfred & Knapp, Michael & Wildenauer, Nicole, 2005. "Auswirkungen unterschiedlicher Assetkorrelationen in Mehr-Sektoren-Kreditportfoliomodellen," University of Regensburg Working Papers in Business, Economics and Management Information Systems 409, University of Regensburg, Department of Economics.
    4. Wang, Zheqi & Crook, Jonathan & Andreeva, Galina, 2020. "Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default," European Journal of Operational Research, Elsevier, vol. 287(2), pages 725-738.
    5. Dunbar, Kwamie, 2012. "Forecasting and Stress-testing the Risk-based Capital Requirements for Revolving Retail Exposures," Working Papers 2012001, Sacred Heart University, John F. Welch College of Business.
    6. Bellotti, Tony & Crook, Jonathan, 2011. "Forecasting and Stress Testing Credit Card Default Using Dynamic Models," Working Papers 11-34, University of Pennsylvania, Wharton School, Weiss Center.
    7. Malik, Madhur & Thomas, Lyn C., 2012. "Transition matrix models of consumer credit ratings," International Journal of Forecasting, Elsevier, vol. 28(1), pages 261-272.
    8. Yi-Ping Chang & Chih-Tun Yu, 2014. "Bayesian confidence intervals for probability of default and asset correlation of portfolio credit risk," Computational Statistics, Springer, vol. 29(1), pages 331-361, February.
    9. Siyi Wang & Xing Yan & Bangqi Zheng & Hu Wang & Wangli Xu & Nanbo Peng & Qi Wu, 2021. "Risk and return prediction for pricing portfolios of non-performing consumer credit," Papers 2110.15102, arXiv.org.
    10. Bellotti, Tony & Crook, Jonathan, 2013. "Forecasting and stress testing credit card default using dynamic models," International Journal of Forecasting, Elsevier, vol. 29(4), pages 563-574.
    11. Palombini, Edgardo, 2009. "Factor models and the credit risk of a loan portfolio," MPRA Paper 20107, University Library of Munich, Germany.
    12. Gamba-Santamaria, Santiago & Melo-Velandia, Luis Fernando & Orozco-Vanegas, Camilo, 2024. "Decomposition of non-performing loans dynamics into a debt-servicing capacity and a risk taking indicators," The Quarterly Review of Economics and Finance, Elsevier, vol. 96(C).
    13. Cho, Yongbok & Lee, Yongwoong, 2022. "Asymmetric asset correlation in credit portfolios," Finance Research Letters, Elsevier, vol. 49(C).
    14. J. Crook & T. Bellotti, 2012. "Asset correlations for credit card defaults," Applied Financial Economics, Taylor & Francis Journals, vol. 22(2), pages 87-95, January.
    15. Pawel Siarka, 2021. "Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge," Mathematics, MDPI, vol. 9(5), pages 1-19, March.
    16. Vahid Baradaran & Maryam Keshavarz, 2017. "System dynamics modelling of retailers' credit risk," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 26(3), pages 380-396.
    17. Petrus Strydom, 2017. "Macro economic cycle effect on mortgage and personal loan default rates," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(6), pages 1-1.
    18. Santiago Gamba-Santamaria & Luis Fernando Melo-Velandia & Camilo Orozco-Vanegas, 2021. "What can credit vintages tell us about non-performing loans?," Borradores de Economia 1154, Banco de la Republica de Colombia.
    19. Alejandro Ferrer Pérez & José Casals Carro & Sonia Sotoca López, 2014. "A new approach to the unconditional measurement of default risk," Documentos de Trabajo del ICAE 2014-11, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    20. Jonathan Crook & Tony Bellotti, 2010. "Time varying and dynamic models for default risk in consumer loans," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 283-305, April.
    21. Vahid Baradaran & Maryam Keshavarz, 2015. "An integrated approach of system dynamics simulation and fuzzy inference system for retailers’ credit scoring," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 28(1), pages 959-980, January.

    More about this item

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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