Optimal Execution in Hong Kong Given a Market-on-Close Benchmark

Quantitative Finance 18 (2018), 655-671

29 Pages Posted: 12 Jun 2015 Last revised: 12 Mar 2018

See all articles by Christoph Frei

Christoph Frei

University of Alberta - Department of Mathematical and Statistical Sciences

Nicholas Westray

New York University (NYU) - Courant Institute of Mathematical Sciences

Date Written: April 27, 2017

Abstract

For stocks traded on the Hong Kong Exchange, the median of five prices taken over the last minute of trading is currently chosen as the closing price. We introduce a stochastic control formulation to target such a median benchmark in an empirically justified model which takes the key microstructural features into account. We solve this problem by providing an explicit and efficient algorithm which even has applications beyond this paper as it can be used for the dynamic linear approximation of any square-integrable random variable. Implementing the algorithm on the stocks of the Hang Seng Index, we find an average improvement of around 6% in standard deviation of slippage compared to an average trader's execution. We conclude by providing a novel decomposition of the trading risk into that which is intrinsic to the median benchmark and that due to execution.

Keywords: Optimal trade execution, Hong Kong Exchange, median benchmark, algorithmic trading

JEL Classification: G15, C61

Suggested Citation

Frei, Christoph and Westray, Nicholas, Optimal Execution in Hong Kong Given a Market-on-Close Benchmark (April 27, 2017). Quantitative Finance 18 (2018), 655-671, Available at SSRN: https://ssrn.com/abstract=2617019 or http://dx.doi.org/10.2139/ssrn.2617019

Christoph Frei (Contact Author)

University of Alberta - Department of Mathematical and Statistical Sciences ( email )

Edmonton, Alberta T6G 2G1
Canada

HOME PAGE: http://www.math.ualberta.ca/~cfrei/

Nicholas Westray

New York University (NYU) - Courant Institute of Mathematical Sciences ( email )

251 Mercer Street
New York, NY - 10012
United States

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