Empirical Analyses on Federal Thrift Savings Plan Portfolio Optimization

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dc.contributor.advisorFu, Michael Cen_US
dc.contributor.advisorMadan, Dilip Ben_US
dc.contributor.authorNestler, Scott Ten_US
dc.contributor.departmentBusiness and Management: Decision & Information Technologiesen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2008-04-22T16:06:57Z
dc.date.available2008-04-22T16:06:57Z
dc.date.issued2007-11-27en_US
dc.description.abstractThere is ample historical data to suggest that log returns of stocks and indices are not independent and identically distributed Normally, as is commonly assumed. Instead, the returns of financial assets are skewed and have higher kurtosis. To account for skewness and excess kurtosis, it is necessary to have a distribution that is more flexible than the Gaussian distribution and uses additional information that may be present in higher moments. The federal government's Thrift Savings Plan (TSP) is the largest defined contribution retirement savings and investment plan, with nearly 3.6 million participants and over $173 billion in assets. The TSP offers five assets (government bond fund, fixed income fund, large-cap stock fund, small-cap stock fund, and international stock fund) to U.S. government civilian employees and uniformed service members. The limited choice of investments, in comparison to most 401(k) plans, may be disappointing from a participant's perspective; however, it provides an attractive framework for empirical study. In this study, we investigate how the optimal choice of TSP assets changes when traditional portfolio optimization methods are replaced with newer techniques. Specifically, the following research questions are posed and answered: (1) Does use of a non-Gaussian factor model for returns, generated with independent components analysis (ICA) and following the Variance Gamma (VG) process, provide any advantage in constructing optimal TSP portfolios? (2) Can excess TSP portfolio returns be generated through rebalancing to an optimal mix? If so, what frequency of rebalancing provides benefits that offset increased computationalal and administrative burden? (3) How does the use of coherent risk and portfolio performance measures, in place of variance as the traditional the measure for risk and Sharpe Ratio as the usual portfolio performance measure affect TSP portfolio selection? We show through simulation that some of the newer schemes should produce excess returns over conventional (mean-variance optimization with Normally-distributed returns) portfolio choice models. The use of some or all of these methods could benefit the nearly 4 million TSP participants in achieving their retirement savings and investment objectives. Furthermore, we propose two new portfolio performance measures based on recent developments in coherent measures of risk.en_US
dc.format.extent1200417 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/7749
dc.language.isoen_US
dc.subject.pqcontrolledOperations Researchen_US
dc.subject.pqcontrolledEconomics, Financeen_US
dc.subject.pqcontrolledMathematicsen_US
dc.subject.pquncontrolledportfolioen_US
dc.subject.pquncontrolledoptimizationen_US
dc.subject.pquncontrolledpensionen_US
dc.subject.pquncontrolledVariance Gammaen_US
dc.subject.pquncontrolledICAen_US
dc.subject.pquncontrolledThrift Savingsen_US
dc.titleEmpirical Analyses on Federal Thrift Savings Plan Portfolio Optimizationen_US
dc.typeDissertationen_US

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