Dark pool trading : stochastic control meets adaptive allocation

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Abstract/Contents

Abstract
This thesis addresses an optimal liquidation problem in which a trader has access to a lit market, which offers guaranteed execution at a worse price, and dark pools, which offer the best price but do not guarantee execution. Given a fixed time window in which to liquidate her inventory, the trader must balance between these two venues to maximize her utility, while simultaneously estimating the inner workings of the dark pool from past execution data. The value function of this optimal control problem satisfies a nonlocal Hamilton-Jacobi-Bellman equation, and resolving the optimal strategy requires an exploration-exploitation assessment of the dark pools. Throughout, algorithmic complexity is kept as low as possible, with an eye towards the high-frequency setting.

Description

Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2021; ©2021
Publication date 2021; 2021
Issuance monographic
Language English

Creators/Contributors

Author Perlman, Mark Palmer
Degree supervisor Papanicolaou, George
Thesis advisor Papanicolaou, George
Thesis advisor Ryzhik, Leonid
Thesis advisor Ying, Lexing
Degree committee member Ryzhik, Leonid
Degree committee member Ying, Lexing
Associated with Stanford University, Department of Mathematics

Subjects

Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Mark Perlman.
Note Submitted to the Department of Mathematics.
Thesis Thesis Ph.D. Stanford University 2021.
Location https://purl.stanford.edu/gg202zz3763

Access conditions

Copyright
© 2021 by Mark Palmer Perlman
License
This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).

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