Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/34773
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dc.titleDiscrete Choice and Portfolio Optimization Under Limited Distributional Information
dc.contributor.authorVINIT KUMAR MISHRA
dc.date.accessioned2012-09-30T18:01:18Z
dc.date.available2012-09-30T18:01:18Z
dc.date.issued2012-06-15
dc.identifier.citationVINIT KUMAR MISHRA (2012-06-15). Discrete Choice and Portfolio Optimization Under Limited Distributional Information. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/34773
dc.description.abstractThe semiparametric Marginal Distribution Choice Models (MDM) are shown to give Generalized Extreme Value models and its popular special case Multinomial Logit Model (MNL). The MDM has nice structural properties for seller's pricing problem, and has computational advantages over simulation-based models such as Probit and mixed-logit for parameter estimation problem. The semiparametric Cross Moment Model (CMM) uses mean and covariance matrix of utilities and performs choice prediction using a Semidefinite program (SDP). The CMM nicely captures utility correlations and produces results comparable to Multinomial Probit, Nested Logit etc. when utilities are correlated. The results obtained by solving a packaging design problem using CMM and MNP show the strength of the CMM. The CMM SDP involves a large number of variables. Using SDP reduction techniques we propose a reduced but exact SDP for the CMM. A portfolio optimization problem is solved using this result with CVaR disutility under regret loss function. The results suggest that this model has high value under turbulent market conditions.
dc.language.isoen
dc.subjectDiscrete Choice, Portfolio Optimization, Persistency Models, CVaR of Regret, Semiparametric Choice, Conjoint Choice, Packaging Design, SDP reduction
dc.typeThesis
dc.contributor.departmentDECISION SCIENCES
dc.contributor.supervisorTEO CHUNG PIAW
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Ph.D Theses (Open)

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