Regularization in econometrics and finance

dc.contributor.advisorCarvalho, Carlos Marinho, 1978-
dc.contributor.committeeMemberHahn, P. Richard
dc.contributor.committeeMemberScott, James
dc.contributor.committeeMemberWilliamson, Sinead
dc.contributor.committeeMemberTitman, Sheridan
dc.creatorPuelz, David Walker
dc.creator.orcid0000-0002-9309-5664
dc.date.accessioned2018-08-07T20:28:06Z
dc.date.available2018-08-07T20:28:06Z
dc.date.created2018-05
dc.date.issued2018-05-02
dc.date.submittedMay 2018
dc.date.updated2018-08-07T20:28:06Z
dc.description.abstractThis dissertation develops regularization methods for use in finance and econometrics problems. The key methodology introduced is utility-based selection (UBS) -- a procedure for inducing sparsity in statistical models and practical problems requiring the need for simple and parsimonious decisions. The introduction section describes statistical model selection in light of the "big data hype" and desire to fit rich and complex models. Key emphasis is placed on the fundamental bias-variance tradeoff in statistics. The remaining portions of the introduction tie these notions into the components and procedure of UBS. This latter half frames model selection as a decision and develops the procedure using decision-theoretic principles. The second chapter applies UBS to portfolio optimization. A dynamic portfolio construction framework is presented, and the asset returns are modeled using a Bayesian dynamic linear model. The focus here is constructing simple, or sparse, portfolios of passive funds. We consider a set of the most liquid exchange traded funds for our empirical analysis. The third chapter discusses variable selection in seemingly unrelated regression models (SURs). UBS is applied in this context where an analyst wants to find, among p available predictors, what subset are most relevant for describing variation in q different responses. The selection procedure takes into account uncertainty in both the responses and predictors. It is applied to a popular problem in asset pricing -- discovering which factors (predictors) are relevant for pricing the cross section of asset returns (responses). We also discuss future work in monotonic function estimation and how UBS is applied in this context. The fourth chapter considers regularization in treatment effect estimation using linear regression. It introduces "regularization-induced confounding" (RIC), a pitfall of employing naive regularization techniques for estimating a treatment effect from observational data. A new model parameterization is presented that mitigates RIC. Additionally, we discuss recent work that considers uncertainty characterization when model errors may vary by clusters of data. These developments employ empirical-Bayes and bootstrapping techniques.
dc.description.departmentInformation, Risk, and Operations Management (IROM)
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T2R20SD9T
dc.identifier.urihttp://hdl.handle.net/2152/65998
dc.language.isoen
dc.subjectUtility-based posterior summarization
dc.subjectAsset pricing
dc.subjectCross-section of returns
dc.titleRegularization in econometrics and finance
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentInformation, Risk, and Operations Management (IROM)
thesis.degree.disciplineStatistics
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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