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Default contagion among credit modalities: evidence from Brazilian data

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  • Alexandre, Michel
  • Antônio Silva Brito, Giovani
  • Cotrim Martins, Theo

Abstract

The aim of this paper is to assess the impact of the default of some personal credit modality in the future default of the other modalities. Using Brazilian microdata, we run a logistic regression to estimate the probability of default in a given credit modality, including among the explanatory variables the personal overdue exposure in the other credit modalities. Our results show that such effect is positive and significant, although quantitatively heterogeneous. We also discuss the rationale behind these results. Specifically, it was found that financing credit modalities (vehicle and real estate financing) contaminate more the other credit modalities, as their default may bring to the debtor the loss of the financed good. Moreover, riskier loan categories (overdraft, non-payroll-deducted personal credit and credit card) are more contaminated by the default of other modalities, what is explained by the fact that defaulted individuals have a limited access to less risky credit modalities.

Suggested Citation

  • Alexandre, Michel & Antônio Silva Brito, Giovani & Cotrim Martins, Theo, 2017. "Default contagion among credit modalities: evidence from Brazilian data," MPRA Paper 76859, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:76859
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    References listed on IDEAS

    as
    1. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    2. Leow, Mindy & Crook, Jonathan, 2016. "The stability of survival model parameter estimates for predicting the probability of default: Empirical evidence over the credit crisis," European Journal of Operational Research, Elsevier, vol. 249(2), pages 457-464.
    3. Correa, Arnildo & Marins, Jaqueline & Neves, Myrian & da Silva, Antonio Carlos, 2014. "Credit Default and Business Cycles: An Empirical Investigation of Brazilian Retail Loans," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 68(3), September.
    4. Jarrow, Robert A. & Turnbull, Stuart M., 2000. "The intersection of market and credit risk," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 271-299, January.
    5. repec:fgv:epgrbe:v:68:n:3:a:3 is not listed on IDEAS
    6. Tony Bellotti & Jonathan Crook, 2014. "Retail credit stress testing using a discrete hazard model with macroeconomic factors," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 340-350, March.
    7. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.
    8. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Credit default contagion; debtor approach; transaction approach;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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