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Transition matrix models of consumer credit ratings

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  • Malik, Madhur
  • Thomas, Lyn C.

Abstract

Although the corporate credit risk literature includes many studies modelling the change in the credit risk of corporate bonds over time, there has been far less analysis of the credit risk for portfolios of consumer loans. However, behavioural scores, which are calculated on a monthly basis by most consumer lenders, are the analogues of ratings in corporate credit risk. Motivated by studies of corporate credit risk, we develop a Markov chain model based on behavioural scores for establishing the credit risk of portfolios of consumer loans. Although such models have been used by lenders to develop models for the Basel Accord, nothing has been published in the literature on them. The model which we suggest differs in many respects from the corporate credit ones based on Markov chains — such as the need for a second order Markov chain, the inclusion of economic variables and the age of the loan. The model is applied using data on a credit card portfolio from a major UK bank.

Suggested Citation

  • Malik, Madhur & Thomas, Lyn C., 2012. "Transition matrix models of consumer credit ratings," International Journal of Forecasting, Elsevier, vol. 28(1), pages 261-272.
  • Handle: RePEc:eee:intfor:v:28:y:2012:i:1:p:261-272
    DOI: 10.1016/j.ijforecast.2011.01.007
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    References listed on IDEAS

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    Cited by:

    1. Maria Rocha Sousa & Jo~ao Gama & Manuel J. Silva Gonc{c}alves, 2014. "A two-stage model for dealing with temporal degradation of credit scoring," Papers 1406.7775, arXiv.org.
    2. Forster, Jonathan J. & Buzzacchi, Matteo & Sudjianto, Agus & Nagao, Risa, 2016. "Modelling credit grade migration in large portfolios using cumulative t-link transition models," European Journal of Operational Research, Elsevier, vol. 254(3), pages 977-984.
    3. Ptak-Chmielewska Aneta & Matuszyk Anna, 2019. "Macroeconomic Factors in Modelling the SMEs Bankruptcy Risk. The Case of the Polish Market," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 40-49, September.
    4. Lapshin, Viktor & Anton, Markov, 2022. "MCMC-based credit rating aggregation algorithm to tackle data insufficiency," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 50-72.
    5. Luis Alberto Merchán Benavides, 2018. "¿Afecta la distancia de residencia a los centros urbanos la calidad en la cartera de creditos? Caso aplicado a una entidad financiera de Colombia," Vniversitas Económica 16451, Universidad Javeriana - Bogotá.
    6. Bocchio, Cecilia & Crook, Jonathan & Andreeva, Galina, 2023. "The impact of macroeconomic scenarios on recurrent delinquency: A stress testing framework of multi-state models for mortgages," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1655-1677.
    7. Maria Rocha Sousa & João Gama & Elísio Brandão, 2013. "Introducing time-changing economics into credit scoring," FEP Working Papers 513, Universidade do Porto, Faculdade de Economia do Porto.
    8. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
    9. David Conaly Martínez Vázquez & Christian Bucio Pacheco & Alejandra Cabello Rosales, 2021. "Proyección Markoviana para 2020 y 2021 de las Calificaciones Corporativas en México," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(1), pages 1-21, Enero - M.
    10. Kheiri Chari , Mohammad & Aliheidari Bioki , Tahereh & Khademizare , Hasan, 2013. "The New Method for Ranking Grouped Credit Customer Based on DEA Method," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 8(4), pages 75-98, October.
    11. 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.
    12. He, Ping & Hua, Zhongsheng & Liu, Zhixin, 2015. "A quantification method for the collection effect on consumer term loans," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 17-26.
    13. David Conaly Martínez Vázquez & Christian Bucio Pacheco & Alejandra Cabello Rosales, 2021. "Proyección Markoviana para 2020 y 2021 de las Calificaciones Corporativas en México," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(1), pages 1-21, Enero - M.
    14. 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.

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