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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. Palombini, Edgardo, 2009. "Factor models and the credit risk of a loan portfolio," MPRA Paper 20107, University Library of Munich, Germany.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. Cho, Yongbok & Lee, Yongwoong, 2022. "Asymmetric asset correlation in credit portfolios," Finance Research Letters, Elsevier, vol. 49(C).
    8. J. Crook & T. Bellotti, 2012. "Asset correlations for credit card defaults," Applied Financial Economics, Taylor & Francis Journals, vol. 22(2), pages 87-95, January.
    9. Pawel Siarka, 2021. "Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge," Mathematics, MDPI, vol. 9(5), pages 1-19, March.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Malik, Madhur & Thomas, Lyn C., 2012. "Transition matrix models of consumer credit ratings," International Journal of Forecasting, Elsevier, vol. 28(1), pages 261-272.
    18. 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.
    19. 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.
    20. 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.
    21. 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.

    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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