2026-09-19T09:15:38Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/551122022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Daniela Pucci de Farias.
Jeria, David (David O. Jeria López)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-05-25T20:43:46Z
2010-05-25T20:43:46Z
2008
2009
http://hdl.handle.net/1721.1/55112
591409779
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 43-44).
We study the problem of optimal execution within a dynamic programming framework. Given an exponential objective function, system variables which are normally distributed, and linear market dynamics, we derive a closed form solution for optimal trading trajectories. We show that a trader lacking private information has trajectories which are static in nature, whilst a trader with private information requires real time observations to execute optimally. We further show that Bellman's equations become increasingly complex to solve if either the market dynamics are nonlinear, or if additional constraints are added to the problem. As such, we propose an approximate dynamic program using linear programming which achieves near-optimality. The algorithm approximates the exponential objective function within a class of linear architectures, and takes advantage of a probabilistic constraint sampling scheme in order to terminate. The performance of the algorithm relies on the quality of the approximation, and as such we propose a set of heuristics for its efficient implementation.
by David Jeria.
M.Eng.
44 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
An approximate dynamic programming approach to risk sensitive control of execution costs
Thesis
application/pdf
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="867c3ed1-a3c5-4053-a969-421fe9145f70">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>An approximate dynamic programming approach to risk sensitive control of execution costs</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2009</PublicationDate>
<Authors>
<Author>
<DisplayName>Jeria, David (David O. Jeria López)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>We study the problem of optimal execution within a dynamic programming framework. Given an exponential objective function, system variables which are normally distributed, and linear market dynamics, we derive a closed form solution for optimal trading trajectories. We show that a trader lacking private information has trajectories which are static in nature, whilst a trader with private information requires real time observations to execute optimally. We further show that Bellman's equations become increasingly complex to solve if either the market dynamics are nonlinear, or if additional constraints are added to the problem. As such, we propose an approximate dynamic program using linear programming which achieves near-optimality. The algorithm approximates the exponential objective function within a class of linear architectures, and takes advantage of a probabilistic constraint sampling scheme in order to terminate. The performance of the algorithm relies on the quality of the approximation, and as such we propose a set of heuristics for its efficient implementation.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>