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Exposición al default: estimación para un portafolio de tarjeta de crédito
[Exposure to default: estimation for a credit card portfolio]

Author

Listed:
  • Bambino-Contreras, Carlos
  • Morales-Oñate, Víctor

Abstract

This work estimates the exposure at default of a credit card portfolio of an Ecuadorian bank without using the credit conversion factor, a common mechanism used in the expected loss distribution estimation literature and suggested by the Basel Committee. To achieve this goal, the probability distribution of this variable (exposure at default) has been identified so that it can be used in the context of generalized linear models. The results show that the model can be used to make predictions based on assumptions closer to the reality of customer behavior based on the variables used in the regression.

Suggested Citation

  • Bambino-Contreras, Carlos & Morales-Oñate, Víctor, 2021. "Exposición al default: estimación para un portafolio de tarjeta de crédito [Exposure to default: estimation for a credit card portfolio]," MPRA Paper 112333, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:112333
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    References listed on IDEAS

    as
    1. Yuta Tanoue & Satoshi Yamashita & Hideaki Nagahata, 2020. "Comparison study of two-step LGD estimation model with probability machines," Risk Management, Palgrave Macmillan, vol. 22(3), pages 155-177, September.
    2. Michael Phelan, 1997. "Probability and Statistics Applied to the Practice of Financial Risk Management: The Case of J.P. Morgan's RiskMetrics™," Journal of Financial Services Research, Springer;Western Finance Association, vol. 12(2), pages 175-200, October.
    3. Tong, Edward N.C. & Mues, Christophe & Brown, Iain & Thomas, Lyn C., 2016. "Exposure at default models with and without the credit conversion factor," European Journal of Operational Research, Elsevier, vol. 252(3), pages 910-920.
    4. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Expected loss; Credit risk; Exposure at default; Generalized linear models; Gamma Distribution; Machine Learning;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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