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Comparison study of two-step LGD estimation model with probability machines

Author

Listed:
  • Yuta Tanoue

    (Waseda University)

  • Satoshi Yamashita

    (The Institute of Statistical Mathematics)

  • Hideaki Nagahata

    (The Institute of Statistical Mathematics)

Abstract

Accurate estimation of loss given default is necessary to estimating credit risk. Due to the bi-modal nature of LGD, the two-step LGD estimation model is a promising method for LGD estimation. This study improves the first model in the two-step LGD estimation model using probability machines (random forest, k-nearest neighbors, bagged nearest neighbors, and support vector machines). Furthermore, we compare the predictive performance of each model with traditional logistic regression models. This study confirms that random forest is the best model for developing the first model in the two-step LGD estimation model.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:risman:v:22:y:2020:i:3:d:10.1057_s41283-020-00059-y
    DOI: 10.1057/s41283-020-00059-y
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    Cited by:

    1. 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.

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