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

Yuta Tanoue, Satoshi Yamashita, Hideaki Nagahata

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)155-177
Number of pages23
JournalRisk Management
Volume22
Issue number3
DOIs
Publication statusPublished - 2020 Sep 1
Externally publishedYes

ASJC Scopus subject areas

  • Business and International Management
  • Finance
  • Economics and Econometrics
  • Strategy and Management

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