Credit risk measurement: application of the logistic regression model to determine the probability of default
Russo, Francesca (A.A. 2020/2021) Credit risk measurement: application of the logistic regression model to determine the probability of default. Tesi di Laurea in Risk management, Luiss Guido Carli, relatore Daniele Penza, pp. 100. [Master's Degree Thesis]
Full text for this thesis not available from the repository.
Abstract/Index
Credit risk. Definition and typologies. Expected loss (EL) & Unexpected loss (UL). Based regulatory framework. IFRS 9: impairment model. Introduction to credit scoring. Credit scoring models. Performance and predictive ability of credit scoring models. Logistic regression analysis. Application of the logistic regression model. Application of the logistic regression model with the exclusion of non-significant variables.
References
Bibliografia: pp. 83-87.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree program in Corporate Finance, English language (LM-77) |
| Chair: | Risk management |
| Thesis Supervisor: | Penza, Daniele |
| Thesis Co-Supervisor: | Pallini, Alfredo |
| Academic Year: | 2020/2021 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 31 May 2022 15:16 |
| Last Modified: | 31 May 2022 15:16 |
| URI: | https://tesi.luiss.it/id/eprint/32573 |
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