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Structural models in consumer credit

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  • de Andrade, Fabio Wendling Muniz
  • Thomas, Lyn

Abstract

We propose a structural credit risk model for consumer lending using option theory and the concept of the value of the consumer’s reputation. Using Brazilian empirical data and a credit bureau score as proxy for creditworthiness we compare a number of alternative models before suggesting one that leads to a simple analytical solution for the probability of default. We apply the proposed model to portfolios of consumer loans introducing a factor to account for the mean influence of systemic economic factors on individuals. This results in a hybrid structural-reduced-form model. And comparisons are made with the Basel II approach. Our conclusions partially support that approach for modelling the credit risk of portfolios of retail credit.
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Suggested Citation

  • de Andrade, Fabio Wendling Muniz & Thomas, Lyn, 2007. "Structural models in consumer credit," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1569-1581, December.
  • Handle: RePEc:eee:ejores:v:183:y:2007:i:3:p:1569-1581
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    Cited by:

    1. Malik, Madhur & Thomas, Lyn C., 2012. "Transition matrix models of consumer credit ratings," International Journal of Forecasting, Elsevier, vol. 28(1), pages 261-272.
    2. Luis H. R. Alvarez & Jani Sainio, 2010. "A Loan Portfolio Model Subject to Random Liabilities and Systemic Jump Risk," Papers 1006.0863, arXiv.org.
    3. Thomas, Lyn C., 2009. "Modelling the credit risk for portfolios of consumer loans: Analogies with corporate loan models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2525-2534.
    4. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    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 016451, Universidad Javeriana - Bogotá.
    6. 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.
    7. P Beling & G Overstreet & K Rajaratnam, 2010. "Estimation error in regulatory capital requirements: theoretical implications for consumer bank profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 381-392, March.
    8. Chen, Shou & Jiang, Xiangqian & He, Hongbo & Zhou, Xi, 2020. "A pricing model with dynamic repayment flows for guaranteed consumer loans," Economic Modelling, Elsevier, vol. 91(C), pages 1-11.
    9. L C Thomas, 2010. "Consumer finance: challenges for operational research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 41-52, January.
    10. Huh, Jaeyung & Chang, Woojin & Lee, Junghoon & Lee, Jaeyong, 2010. "Samsung card lending model," European Journal of Operational Research, Elsevier, vol. 207(1), pages 492-498, November.

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    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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