抜粋
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.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 155-177 |
| ページ数 | 23 |
| ジャーナル | Risk Management |
| 巻 | 22 |
| 発行部数 | 3 |
| DOI | |
| 出版物ステータス | Published - 2020 9 1 |
ASJC Scopus subject areas
- Business and International Management
- Finance
- Economics and Econometrics
- Strategy and Management
フィンガープリント Comparison study of two-step LGD estimation model with probability machines' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。
これを引用
Tanoue, Y., Yamashita, S., & Nagahata, H. (2020). Comparison study of two-step LGD estimation model with probability machines. Risk Management, 22(3), 155-177. https://doi.org/10.1057/s41283-020-00059-y