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Decision support modeling via multivariate risk measures and stochastic optimization
Descriptive
TitleInfo
Title
Decision support modeling via multivariate risk measures and stochastic optimization
Name
(type = personal)
NamePart
(type = family)
Lee
NamePart
(type = given)
Jinwook
NamePart
(type = date)
1978-
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Jinwook Lee
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author
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Prekopa
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Andras
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Andras Prekopa
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Advisory Committee
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chair
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Boros
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Endre
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Endre Boros
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Advisory Committee
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internal member
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Gurvich
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Vladimir
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Vladimir Gurvich
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Advisory Committee
Role
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internal member
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(type = family)
Gursoy
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Kemal
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Kemal Gursoy
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Advisory Committee
Role
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(authority = RULIB)
internal member
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Unuvar
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Merve
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Merve Unuvar
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Advisory Committee
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outside member
Name
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Rutgers University
Role
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degree grantor
Name
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NamePart
Graduate School - New Brunswick
Role
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school
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Text
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theses
OriginInfo
DateCreated
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2014
DateOther
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(type = degree)
2014-05
Place
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xx
Language
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eng
Abstract
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Whenever we have a decision to make, there is always some risk to take. From a mathematical perspective, risk is manifested by a random variable, and a risk measure simply characterizes the random variable in a more compact form. Risk, in general and in practice, is not be adequately described by a real valued random variable, but rather requires a random vector to capture the dimensions of the problem. To this end, multivariate risk measures are crucial ingredients for decision making processes, and stochastic optimization is a natural and superior skill to find a key to the optimal decision-making. A recent paper by Pr ekopa (2012) presented results in connection with Multivariate Value-at-Risk (MVaR) that has been known for some time under the name of p-quantile or p-Level E cient Point (pLEP) and introduced a new multivariate risk measure, called Multivariate Conditional Value-at-Risk (MCVaR). Lee and Pr ekopa (2013) studied new methods for numerical calculations and mathematical properties of these multivariate risk measures, presented in Chapter 2. Another new multivariate risk measure has been constructed and presented in Chapter 3. This is especially for corporate mergers and acquisition (M&A) transactions, as the limited applicability of a coherent risk measure in the sense of Artzner et al (1999) for M&A transactions is already discussed in Kou et al (2013). A decision making scheme using that risk measure is introduced and surveyed, together with illustrative real-life numerical examples. Insurance companies typically hold their money in bonds to pay out the random liabilities in the same periods. In Chapter 4, such bond portfolio construction problem is presented using various stochastic programming problem formulations. For a financial trading business, "price-bands" can be used as an indicator for successfully buying or short-selling shares of stock. Chapter 5 presents a mathematical model for the novel construction of price-bands using a stochastic programming formulation. Numerical examples using recent US stock market intraday data are presented.
Subject
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Topic
Operations Research
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_5385
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electronic resource
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application/pdf
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text/xml
Extent
x, 116 p. : ill.
Note
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Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Jinwook Lee
Subject
(authority = ETD-LCSH)
Topic
Stochastic processes
Subject
(authority = ETD-LCSH)
Topic
Risk
Subject
(authority = ETD-LCSH)
Topic
Risk management
RelatedItem
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier
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doi:10.7282/T3RN365B
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Lee
GivenName
Jinwook
Role
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Permission or license
DateTime
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2014-04-04 17:00:21
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Name
Jinwook Lee
Role
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Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
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windows xp
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