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Risk-averse optimal control of diffusion processes

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TitleInfo
Title
Risk-averse optimal control of diffusion processes
Name (type = personal)
NamePart (type = family)
Yao
NamePart (type = given)
Jianing
NamePart (type = date)
1988-
DisplayForm
Jianing Yao
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Ruszczynski
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Andrzej
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Andrzej Ruszczynski
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Advisory Committee
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chair
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Ocone
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Daniel
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Daniel Ocone
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Advisory Committee
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internal member
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NamePart (type = family)
Yang
NamePart (type = given)
Jian
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Jian Yang
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Advisory Committee
Role
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internal member
Name (type = personal)
NamePart (type = family)
Lin
NamePart (type = given)
Xiaodong
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Xiaodong Lin
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Advisory Committee
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internal member
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Dentcheva
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Darinka
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Darinka Dentcheva
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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Graduate School - Newark
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Text
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theses
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2017
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2017-05
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2017
Place
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xx
Language
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eng
Abstract (type = abstract)
This work analyzes an optimal control problem for which the performance is measured by a dynamic risk measure. While dynamic risk measures in discrete-time and the control problems associated are well understood, the continuous-time framework brings great challenges both in theory and practice. This study addresses modeling, numerical schemes and applications. In the first part, we focus on the formulation of a risk-averse control problem. Specifically, we make use of a decoupled forward-backward system of stochastic differential equations to evaluate a fixed policy: the forward stochastic differential equation (SDE) characterizes the evolution of states, and the backward stochastic differential equation (BSDE) does the risk evaluation at any instant of time. Relying on the Markovian structure of the system, we obtain the corresponding dynamic programming equation via weak formulation and strong formulation; in the meanwhile, the risk-averse Hamilton-Jacobi-Bellman equation and its verification are derived under suitable assumptions. In the second part, the main thrust is to find a convergent numerical method to solve the system in discrete-time setting. Specifically, we construct a piecewise-constant Markovian control to show its arbitrarily closeness to the optimal control. The results heavily relies on the regularity of the solution to generalized Hamilton-Jacobi-Bellman PDE. In the third part, we propose a numerical method for risk evaluation defined by BSDE. Using dual representation of the risk measure, we converted risk valuation to a stochastic control problem, where the control is the Radon-Nikodym derivative process. The optimality conditions of such control problem enables us to use a piecewise-constant density (control) to arrive at a close approximation on a short interval. Then, the Bellman principle extends the approximation to any finite time horizon problem. Lastly, we give a financial application in risk management in conjunction with nested simulation.
Subject (authority = RUETD)
Topic
Management
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
Identifier
ETD_7962
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (viii, 86 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Jianing Yao
RelatedItem (type = host)
TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
Identifier (type = local)
rucore10002600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3W66PQ6
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Yao
GivenName
Jianing
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-04-08 13:47:05
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Name
Jianing Yao
Role
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Affiliation
Rutgers University. Graduate School - Newark
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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.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-05-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2019-05-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 31st, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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2017-04-27T19:36:34
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2017-04-27T19:36:34
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