Investigating the Theory of Survival Analysis in Credit Risk Management of Facility Receivers: A Case Study on Tose'e Ta'avon Bank of Guilan Province
Nowadays, one of the most important topics in risk management of banks, financial, and credit institutions is credit risk management. In this research, the researchers used survival analytic methods for credit risk modeling in terms of the conditional distribution function of default time. As a prac...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Islamic Azad University, Rasht Branch
2018-06-01
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| Series: | Iranian Journal of Optimization |
| Subjects: | |
| Online Access: | http://ijo.iaurasht.ac.ir/article_533467_4aa0c93f95471e94893a49df79c61edf.pdf |
| Summary: | Nowadays, one of the most important topics in risk management of banks, financial, and credit institutions is credit risk management. In this research, the researchers used survival analytic methods for credit risk modeling in terms of the conditional distribution function of default time. As a practical task, the authors considered the reward credit portfolio of Tose'e Ta'avon Bank of Guilan Province and estimate the bank’s probability of default based on the survival analysis method. In order to analyze and verify the research hypothesis, firstly, the researcher estimated the survival analysis, survival function, and then the value of the probability of default function by three parametric, semi-parametric (proportional hazards model), and non-parametric methods. Finally, the author compared these three methods by using the ROC method. In order to analyze data, SPSS, SAS, R, and Minitab softwares were used. The results revealed that the parametric model was better and more suitable than the other models. After the parametric model, it was observed that, the semi-parametric model (proportional hazards model) and then the non-parametric model proved to be the best models. The results of this research suggest using rating or credit score in banks, because, in addition to the proper management of allocation of facility to customers, using a credit score as an explanatory variable can result in more efficient and more accurate estimations of default probability. |
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| ISSN: | 2588-5723 2008-5427 |