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Corporate Default Predictions and Methods for Uncertainty Quantifications

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dc.contributor.authorYuan, Miaoen
dc.contributor.committeechairHong, Yilien
dc.contributor.committeememberSands, Laura P.en
dc.contributor.committeememberZhu, Hongxiaoen
dc.contributor.committeememberKim, Inyoungen
dc.contributor.departmentStatisticsen
dc.date.accessioned2018-01-24T07:00:32Zen
dc.date.available2018-01-24T07:00:32Zen
dc.date.issued2016-08-01en
dc.description.abstractRegarding quantifying uncertainties in prediction, two projects with different perspectives and application backgrounds are presented in this dissertation. The goal of the first project is to predict the corporate default risks based on large-scale time-to-event and covariate data in the context of controlling credit risks. Specifically, we propose a competing risks model to incorporate exits of companies due to default and other reasons. Because of the stochastic and dynamic nature of the corporate risks, we incorporate both company-level and market-level covariate processes into the event intensities. We propose a parsimonious Markovian time series model and a dynamic factor model (DFM) to efficiently capture the mean and correlation structure of the high-dimensional covariate dynamics. For estimating parameters in the DFM, we derive an expectation maximization (EM) algorithm in explicit forms under necessary constraints. For multi-period default risks, we consider both the corporate-level and the market-level predictions. We also develop prediction interval (PI) procedures that synthetically take uncertainties in the future observation, parameter estimation, and the future covariate processes into account. In the second project, to quantify the uncertainties in the maximum likelihood (ML) estimators and compute the exact tolerance interval (TI) factors regarding the nominal confidence level, we propose algorithms for two-sided control-the-center and control-both-tails TI for complete or Type II censored data following the (log)-location-scale family of distributions. Our approaches are based on pivotal properties of ML estimators of parameters for the (log)-location-scale family and utilize the Monte-Carlo simulations. While for Type I censored data, only approximate pivotal quantities exist. An adjusted procedure is developed to compute the approximate factors. The observed CP is shown to be asymptotically accurate by our simulation study. Our proposed methods are illustrated using real-data examples.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:8496en
dc.identifier.urihttp://hdl.handle.net/10919/81905en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectDefault Risken
dc.subjectDynamic Factor Modelen
dc.subjectHigh-Dimensional Time Seriesen
dc.subject(Log)-Location-Scale Distributionsen
dc.subjectTolerance Interval.en
dc.titleCorporate Default Predictions and Methods for Uncertainty Quantificationsen
dc.typeDissertationen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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