دانلود رایگان مقاله پیش بینی اتلاف خودکار با توجه به وام بانکی با مدل چند مرحله

abstract

Probability of default (PD) and loss given default (LGD) are key risk parameters in credit risk management. The majority of LGD research is based on the corporate bond market and few studies focus on the LGD of bank loans even in Japan because of the lack of available public data on bank loan losses. Consequently, knowledge concerning Japanese bank loan LGD is scarce. This study uses Japanese bank loan data to analyze the influencing factors of LGD and to develop a (multi-stage) model to predict LGD and expected loss (EL). We found that collateral, guarantees, and loan size impact LGD. Further, we confirmed that our multistage LGD model has superior predictive accuracy than the corresponding OLS model, Tobit model and Inflated beta regression model. © 2016 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

8. Conclusions

This study has calculated fundamental data and analyzed the relationship between the influencing factors and LGD. Using the data for Japanese bank loans, we built LGD and EL forecasting models and proposed methods for their estimation, taking into account Japanese banking practices. We obtained the following results. The LGD levels of Japanese banks are lower than those suggested by FIRB. The duration of the workout process varies between banks, meaning that the length of observation period for the estimation of LGD that is sufficient differs between banks. Collateral, credit guarantees, and EAD are important factors that influence LGD. We confirmed that the multistage LGD model has a superior predictive accuracy relative to the OLS, Tobit, and inflated beta regression models.