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Volume Weighted Average Price Optimal Execution

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  • Enzo Busseti
  • Stephen Boyd

Abstract

We study the problem of optimal execution of a trading order under Volume Weighted Average Price (VWAP) benchmark, from the point of view of a risk-averse broker. The problem consists in minimizing mean-variance of the slippage, with quadratic transaction costs. We devise multiple ways to solve it, in particular we study how to incorporate the information coming from the market during the schedule. Most related works in the literature eschew the issue of imperfect knowledge of the total market volume. We instead incorporate it in our model. We validate our method with extensive simulation of order execution on real NYSE market data. Our proposed solution, using a simple model for market volumes, reduces by 10% the VWAP deviation RMSE of the standard "static" solution (and can simultaneously reduce transaction costs).

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  • Enzo Busseti & Stephen Boyd, 2015. "Volume Weighted Average Price Optimal Execution," Papers 1509.08503, arXiv.org.
    • Handle: RePEc:arx:papers:1509.08503
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    References listed on IDEAS

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    1. Bialkowski, Jedrzej & Darolles, Serge & Le Fol, Gaëlle, 2008. "Improving VWAP strategies: A dynamic volume approach," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1709-1722, September.
    2. Obizhaeva, Anna A. & Wang, Jiang, 2013. "Optimal trading strategy and supply/demand dynamics," Journal of Financial Markets, Elsevier, vol. 16(1), pages 1-32.
    3. Konishi, Hizuru, 2002. "Optimal slice of a VWAP trade," Journal of Financial Markets, Elsevier, vol. 5(2), pages 197-221, April.
    4. Fabrizio Lillo & J. Doyne Farmer & Rosario N. Mantegna, 2003. "Master curve for price-impact function," Nature, Nature, vol. 421(6919), pages 129-130, January.
    5. Humphery-Jenner, Mark L., 2011. "Optimal VWAP trading under noisy conditions," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2319-2329, September.
    6. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
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    Cited by:

    1. Victor Olkhov, 2022. "Market-Based Asset Price Probability," Papers 2205.07256, arXiv.org, revised Feb 2024.
    2. Olkhov, Victor, 2020. "Classical Option Pricing and Some Steps Further," MPRA Paper 105431, University Library of Munich, Germany, revised 28 Dec 2020.
    3. Olkhov, Victor, 2020. "Price, Volatility and the Second-Order Economic Theory," MPRA Paper 102767, University Library of Munich, Germany.
    4. Victor Olkhov, 2021. "Three Remarks On Asset Pricing," Papers 2105.13903, arXiv.org, revised Jan 2024.
    5. Olkhov, Victor, 2022. "Introduction of the Market-Based Price Autocorrelation," MPRA Paper 112003, University Library of Munich, Germany.
    6. Victor Olkhov, 2020. "Volatility Depend on Market Trades and Macro Theory," Papers 2008.07907, arXiv.org.
    7. Damiano Brigo & Clement Piat, 2016. "Static vs adapted optimal execution strategies in two benchmark trading models," Papers 1609.05523, arXiv.org.
    8. Olkhov, Victor, 2022. "Price and Payoff Autocorrelations in the Consumption-Based Asset Pricing Model," MPRA Paper 112255, University Library of Munich, Germany.
    9. Victor Olkhov, 2022. "Market-Based Price Autocorrelation," Papers 2202.09323, arXiv.org, revised Feb 2024.
    10. Victor Olkhov, 2021. "To VaR, or Not to VaR, That is the Question," Papers 2101.08559, arXiv.org, revised Oct 2021.
    11. Victor Olkhov, 2022. "Why Economic Theories and Policies Fail? Unnoticed Variables and Overlooked Economics," Papers 2208.07839, arXiv.org.
    12. Simon Clinet & Jean-Franc{c}ois Perreton & Serge Reydellet, 2021. "Optimal trading: a model predictive control approach," Papers 2110.11008, arXiv.org, revised Nov 2021.

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