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Title: Survival mixture models for credit risk analysis
Other Titles: Sheng cun hun he mo xing dui xin dai feng xian de fen xi
Authors: Mo, Shek Fung (巫碩峰)
Department: Dept. of Management Sciences
Degree: Master of Philosophy
Issue Date: 2007
Publisher: City University of Hong Kong
Subjects: Credit -- Management
Risk management
Notes: CityU Call Number: HG3751.M6 2007
Includes bibliographical references (leaves 81-85)
Thesis (M.Phil.)--City University of Hong Kong, 2007
vii, 85 p. : ill. ; 30 cm.
Type: Thesis
Abstract: One shortcoming in the existing models for credit scoring is the assumption that all borrowers are at-risk, i.e. they must default in the long run. With this assumption, the models’ performance gets skewed in the sense that the probability of default for “good” borrowers is overestimated. To overcome this problem, the long-term survival mixture model is proposed, which is an extension of the ordinary survival model. In particular, it assumes that only a fraction of the borrowers are at-risk (such fraction is forced to be 100% under the ordinary survival model) and the remaining are “risk-free”. The term “risk-free borrower” does not necessarily refer to a debtor who never defaults, but the one who does not default for a sufficiently long period. In this setting, the probability of being at-risk is modelled via a logistic regression and the time-to-default (for the at-risk group) is modelled under the survival analysis framework with the baseline hazard following a Weibull distribution. With German credit data, the proposed ‘survival mixture model’ is compared with the ‘Cox Proportional Hazards model’, Weibull survival model and logit model by means of C-Statistic, which is the estimated area under the ROC (Receiver Operating Characteristic) curve. The ‘survival mixture model’ shows better, or at least comparable, performance. Simulation study is carried out to investigate the applicability of the mixture model in various situations. It is found that the performance of the estimators is generally acceptable. The survival mixture model not only estimates the regression coefficients in the hazard function, but also predicts the probability of being at-risk. It provides additional information about the borrowers’ default risk, which assists the lending institutions to better manage credit risk.
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