Dark pool trading : stochastic control meets adaptive allocation
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 |
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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 |
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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 |
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Genre | Text |
Bibliographic information
Statement of responsibility | Mark Perlman. |
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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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