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Multi-period scenario generation to support portfolio optimization

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Text
TitleInfo (ID = T-1)
Title
Multi-period scenario generation to support portfolio optimization
SubTitle
PartName
PartNumber
NonSort
Identifier (displayLabel = ); (invalid = )
ETD_2131
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051807
Language (objectPart = )
LanguageTerm (authority = ISO639-2); (type = code)
eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (ID = SBJ-2); (authority = ETD-LCSH)
Topic
Stochastic processes
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
Mathematical optimization
Subject (ID = SBJ-4); (authority = ETD-LCSH)
Topic
Portfolio management--Mathematical models
Abstract
Stochastic Programming (SP) models are widely used for real life problems involving uncertainty. The random nature of problem parameters is modeled via discrete scenarios, which makes the scenario generation process very critical to the success of the overall approach. In this study we consider a portfolio management problem and propose two scenario generation algorithms and a SP model to support investment decisions. The main objective of the scenario generation algorithms is to infer representative probability values to be assigned to the scenario realizations sampled from historical data.
The first algorithm assigns the probabilities by using similarity scores, assigning higher probabilities to the scenarios with data paths that are relatively similar to historical paths, where similarity scores are computed by means of distance measures. We first implement this approach using the weighted Euclidean distance (WED). We also propose a new distance measure to obtain similarity scores as an alternative to WED.
The second scenario generation algorithm is based on the combination of moment-matching technique and the Exponential Generalized Auto-Regressive Conditional Heteroskedasticity (EGARCH) model. Scenario probabilities are assigned such that the first four moments of the sampled returns are fit to target moments through a linear programming model, where the second target moments are set to be conditional on the past scenarios on the scenario tree using the EGARCH model. An additional set of constraints are proposed to increase robustness.
The generated scenarios become input to the SP model to restructure the existing portfolio such that the expected final wealth is maximized and the risk exposure is controlled through constraining Conditional Value-at-Risk at each decision epoch on the scenario tree. We finally propose a generic approach to reduce potential losses and implement it on a logistic regression framework.
PhysicalDescription
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electronic resource
Extent
xviii, 221 p. : ill.
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application/pdf
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Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 216-220)
Note (type = statement of responsibility)
by Erhan Deniz
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Deniz
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Erhan
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1980-
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author
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Erhan Deniz
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Luxhoj
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James
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chair
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Advisory Committee
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James T. Luxhoj
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Boucher
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Thomas
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internal member
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Advisory Committee
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Thomas O. Boucher
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Coit
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David
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internal member
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Advisory Committee
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David W. Coit
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NamePart (type = family)
Papadakis
NamePart (type = given)
Yanni
Role
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outside member
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Advisory Committee
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Yanni Papadakis
Name (ID = NAME-1); (type = corporate)
NamePart
Rutgers University
Role
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degree grantor
Name (ID = NAME-2); (type = corporate)
NamePart
Graduate School - New Brunswick
Role
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school
OriginInfo
DateCreated (point = ); (qualifier = exact)
2009
DateOther (qualifier = exact); (type = degree)
2009-10
Place
PlaceTerm (type = code)
xx
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3SN0949
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (AUTHORITY = GS); (ID = rulibRdec0006)
The author owns the copyright to this work
Copyright
Status
Copyright protected
Notice
Note
Availability
Status
Open
Reason
Permission or license
Note
RightsHolder (ID = PRH-1); (type = personal)
Name
FamilyName
Deniz
GivenName
Erhan
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Copyright holder
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Permission or license
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DateTime
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Name
Erhan Deniz
Affiliation
Rutgers University. Graduate School - New Brunswick
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License
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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