Comparison study of two-step LGD estimation model with probability machines
Comparison study of two-step LGD estimation model with probability machines
复制标题
两步LGD估计模型与概率机的比较研究
DOI：
10.1057/s41283-020-00059-y
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Nagahata Hideaki
中科院分区：
文献类型：
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作者：
Tanoue Yuta;Yamashita Satoshi;Nagahata Hideaki
违约损失率的准确估计是信用风险评估的必要条件。由于LGD的双峰性质，两步LGD估计模型是一种很有前途的LGD估计方法。本研究使用机率机器（随机森林、k-最近邻、袋装最近邻及支持向量机）改进两步骤LGD估计模型中的第一个模型。此外，我们比较了每个模型的预测性能与传统的逻辑回归模型。本研究证实，随机森林是建立两步LGD估计模型中第一个模型的最佳模型。
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.