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Volume 11, Issue 4
A Markov-Driven Portfolio Execution Strategy with Market Impact

Qingqing Yang, Wai-Ki Ching, Tak-Kuen Siu & Zhiwen Zhang

Numer. Math. Theor. Meth. Appl., 11 (2018), pp. 701-728.

Published online: 2018-06

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  • Abstract

In this paper, we propose a framework for studying optimal agency execution strategies in a Limit Order Book (LOB) under a Markov-modulated market environment. The Almgren-Chriss's market impact model [1] is extended to a more general situation where multiple venues are available for investors to submit trades. Under the assumption of risk-neutrality, a compact recursive formula is derived, using the value iterative method, to calculate the optimal agency execution strategy. The original optimal control problem is then converted to a constrained quadratic optimization problem, which can be solved by using the Quadratic Programming (QP) approach. Numerical examples are given to illustrate the efficiency and effective of our proposed methods.

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@Article{NMTMA-11-701, author = {}, title = {A Markov-Driven Portfolio Execution Strategy with Market Impact}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2018}, volume = {11}, number = {4}, pages = {701--728}, abstract = {

In this paper, we propose a framework for studying optimal agency execution strategies in a Limit Order Book (LOB) under a Markov-modulated market environment. The Almgren-Chriss's market impact model [1] is extended to a more general situation where multiple venues are available for investors to submit trades. Under the assumption of risk-neutrality, a compact recursive formula is derived, using the value iterative method, to calculate the optimal agency execution strategy. The original optimal control problem is then converted to a constrained quadratic optimization problem, which can be solved by using the Quadratic Programming (QP) approach. Numerical examples are given to illustrate the efficiency and effective of our proposed methods.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.2018.s02}, url = {http://global-sci.org/intro/article_detail/nmtma/12468.html} }
TY - JOUR T1 - A Markov-Driven Portfolio Execution Strategy with Market Impact JO - Numerical Mathematics: Theory, Methods and Applications VL - 4 SP - 701 EP - 728 PY - 2018 DA - 2018/06 SN - 11 DO - http://doi.org/10.4208/nmtma.2018.s02 UR - https://global-sci.org/intro/article_detail/nmtma/12468.html KW - AB -

In this paper, we propose a framework for studying optimal agency execution strategies in a Limit Order Book (LOB) under a Markov-modulated market environment. The Almgren-Chriss's market impact model [1] is extended to a more general situation where multiple venues are available for investors to submit trades. Under the assumption of risk-neutrality, a compact recursive formula is derived, using the value iterative method, to calculate the optimal agency execution strategy. The original optimal control problem is then converted to a constrained quadratic optimization problem, which can be solved by using the Quadratic Programming (QP) approach. Numerical examples are given to illustrate the efficiency and effective of our proposed methods.

Qingqing Yang, Wai-Ki Ching, Tak-Kuen Siu & Zhiwen Zhang. (2020). A Markov-Driven Portfolio Execution Strategy with Market Impact. Numerical Mathematics: Theory, Methods and Applications. 11 (4). 701-728. doi:10.4208/nmtma.2018.s02
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