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dc.contributor.authorZhang, Aijunen_US
dc.date.accessioned2009-09-03T14:45:38Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2009-09-03T14:45:38Z
dc.date.issued2009en_US
dc.date.submitted2009en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/63707
dc.description.abstractThis research deals with some statistical modeling problems that are motivated by credit risk analysis. Credit risk modeling has been the subject of considerable research interest in finance and has recently drawn the attention of statistical researchers. In the first chapter, we provide an up-to-date review of credit risk models and demonstrate their close connection to survival analysis. The first statistical problem considered is the development of adaptive smoothing spline (AdaSS) for heterogeneously smooth function estimation. Two challenging issues that arise in this context are evaluation of reproducing kernel and determination of local penalty, for which we derive an explicit solution based on piecewise type of local adaptation. Our nonparametric AdaSS technique is capable of fitting a diverse set of `smooth' functions including possible jumps, and it plays a key role in subsequent work in the thesis. The second topic is the development of dual-time analytics for observations involving both lifetime and calendar timescale. It includes "vintage data analysis" (VDA) for continuous type of responses in the third chapter, and "dual-time survival analysis" (DtSA) in the fourth chapter. We propose a maturation-exogenous-vintage (MEV) decomposition strategy in order to understand the risk determinants in terms of self-maturation in lifetime, exogenous influence by macroeconomic conditions, and heterogeneity induced from vintage originations. The intrinsic identification problem is discussed for both VDA and DtSA. Specifically, we consider VDA under Gaussian process models, provide an efficient MEV backfitting algorithm and assess its performance with both simulation and real examples. DtSA on Lexis diagram is of particular importance in credit risk modeling where the default events could be triggered by both endogenous and exogenous hazards. We consider nonparametric estimators, first-passage-time parameterization and semiparametric Cox regression. These developments extend the family of models for both credit risk modeling and survival analysis. We demonstrate the application of DtSA to credit card and mortgage risk analysis in retail banking, and shed some light on understanding the ongoing credit crisis.en_US
dc.format.extent1537110 bytes
dc.format.extent1373 bytes
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypetext/plain
dc.language.isoen_USen_US
dc.subjectCredit Risken_US
dc.subjectDual-time Analyticsen_US
dc.subjectVintage Data Analysisen_US
dc.subjectSurvival Analysisen_US
dc.subjectSmoothing Splineen_US
dc.subjectRetail Bankingen_US
dc.titleStatistical Methods in Credit Risk Modeling.en_US
dc.typeThesisen_US
dc.description.thesisdegreenamePh.D.en_US
dc.description.thesisdegreedisciplineStatisticsen_US
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studiesen_US
dc.contributor.committeememberNair, Vijayan N.en_US
dc.contributor.committeememberSudjianto, Agusen_US
dc.contributor.committeememberHsing, Tailenen_US
dc.contributor.committeememberJin, Jionghuaen_US
dc.contributor.committeememberZhu, Jien_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/63707/1/ajzhang_1.pdf
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


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