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dc.contributor.authorMungasi, Sammy Monyoncho
dc.date.accessioned2021-02-22T12:11:29Z
dc.date.available2021-02-22T12:11:29Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/11071/10163
dc.descriptionA Dissertation submitted in partial fulfillment of the requirements for the Master of Science in Mathematical Finance (MSc.MF) at Strathmore Universityen_US
dc.description.abstractCredit 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 the validity of credit risk models used by many companies when predicting and analysis of loan default is a common phenomenon that occurs unexpectedly. Loan defaults often cause 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 the Cox proportional hazard model and its extensions as well as the mixture cure and non-cure model. We then compare the results systematically by investigating the most efficient awl preferable model that produces best estimates in the Kenyan real data, sets. Results show the Cox Proportional Hazard (Cox PH) model is more efficient in the analysis of Kenyan real data set compared to the frailty, the mixture cure, and non-cure model.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectSurvival analysisen_US
dc.subjectCredit risksen_US
dc.titleComparison of survival analysis approaches to modelling credit risksen_US
dc.typeThesisen_US


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