American Journal of Theoretical and Applied Statistics
Volume 8, Issue 2, March 2019, Pages: 39-46
Received: Apr. 1, 2019;
Accepted: May 15, 2019;
Published: Jun. 5, 2019
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Sammy Mungasi, Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya
Collins Odhiambo, Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya
Credit risk is a critical area in finance and has drawn considerable research attention. As such, survival analysis has widely been used in credit risk, in particular to model debt’s time to default mechanisms. In this study, we revisit different survival analysis approaches as applied in credit risk defaulters’ data and assess their performance in light of the Kenyan context. In practice, inconsistency in validity of credit risk models used by many company when predicting and analysis of loan default is a common phenomenon that occurs unexpectedly. Loan defaults, often causes major loses to creditors’ and can be of great benefit if quantified correctly in advance by using correct models. Here, we address the unbiasedness, analysis and comparison of survival analysis approaches, particularly, the models of credit risk. We carry out data analysis using Cox proportional hazard model and it’s extensions as well as the mixture cure and non-cure model. We then compare the results systematically by investigating the most efficient and preferable model that produces best estimates in Kenyan real data setting. Results show, the Cox Proportional Hazard (CPH) model is more efficient in the analysis of Kenyan real data set compared to the frailty, the mixture cure and non-cure model.
Comparison of Survival Analysis Approaches to Modelling Credit Risks, American Journal of Theoretical and Applied Statistics.
Vol. 8, No. 2,
2019, pp. 39-46.
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