Comparison study of two-step LGD estimation model with probability machines
Yuta Tanoue (),
Satoshi Yamashita () and
Hideaki Nagahata ()
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Yuta Tanoue: Waseda University
Satoshi Yamashita: The Institute of Statistical Mathematics
Hideaki Nagahata: The Institute of Statistical Mathematics
Risk Management, 2020, vol. 22, issue 3, No 1, 155-177
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.
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