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New dynamic optimization models for tax loss valuation and sourcing problems
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Varzgani, Nilofar. New dynamic optimization models for tax loss valuation and sourcing problems. Retrieved from https://doi.org/doi:10.7282/T3K35XRB

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TitleNew dynamic optimization models for tax loss valuation and sourcing problems
NameVarzgani, Nilofar (author); Katehakis, Michael N. (chair); Govindaraj, Suresh (co-chair); Vaidya, Jaideep (internal member); Papadimitriou, Spiros (internal member); Chidambaran, N. K. (outside member); Rutgers University; Graduate School - Newark
Date Created2017
Other Date2017-10 (degree)
SubjectManagement, Mathematical optimization
Extent1 online resource (xi, 86 p. : ill.)
DescriptionThe dissertation comprises of three essays on multi-period dynamic optimization models. The first and second essays address the problem of valuing tax loss-carryforward(s) (TCLFs) and Carryback(s) (TLCBs) that arise frequently in theory and practice. While there have been a number of empirical papers that have shown TLCFs (TLCBs) are value relevant, there is little guidance on how to actually value these. The first essay introduces the valuation problem and provides a survey of literature in the area. The few analytical papers that have attempted to provide valuation formulas are highly stylized, do not capture the institutional complexity of the tax code, and are generally inadequate. In the second essay we develop a finite horizon discrete time stochastic dynamic programming framework for valuing TLCFs (TLCBs) that allows for piece-wise linear progressive taxation, and also incorporates many of the institutional features of the tax code. In the third essay we investigate a multi-product, multi-echelon contract manufacturer based business model. The decision problem faced by the manufacturing company is twofold: (a) how many contract manufacturers to get involved in business with (one supplier model vs a multi-supplier model, (b) how much volume should be allocated to each contract manufacturer, if the multiple supplier model is chosen. The objective is to maximize savings from the volume allocation process. Such problems arise often in many manufacturing industries such as electronics, pharmaceuticals, energy etc., for Original Equipment Manufacturing (OEM) firms. We provide a discrete time stochastic dynamic programming framework, and use numerical methods to study different volume allocation scenarios first in a one period setting, later extending the analysis to a multi-period model.
NotePh.D.
NoteIncludes bibliographical references
Noteby Nilofar Varzgani
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3K35XRB
Languageeng
CollectionGraduate School - Newark Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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