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Improving VWAP strategies: A dynamical volume approach

Author

Listed:
  • Jedrzej Białkowski

    () (Department of Finance, Faculty of Business, Auckland University of Technology)

  • Serge Darolles

    () (Société Générale Asset Management AI, Center for Research in Economics and Statistics (CREST))

  • Gaëlle Le Fol

    () (University of Evry, Center for Research in Economics and Statistics (CREST), and Europlace Institute of Finance)

Abstract

In this paper, we present a new methodology for modeling intraday volume which allows for a reduction of the execution risk in VWAP (Volume Weighted Average Price) orders. The results are obtained for the all stocks included in the CAC40 index at the beginning of September 2004. The idea of considered models is based on the decomposition of traded volume into two parts: one reflects volume changes due to market evolutions, the second describes the stock specific volume pattern. The dynamics of the specific part of volume is depicted by ARMA, and SETAR models. The implementation of VWAP strategies imposes some dynamical adjustments within the day.

Suggested Citation

  • Jedrzej Białkowski & Serge Darolles & Gaëlle Le Fol, 2006. "Improving VWAP strategies: A dynamical volume approach," Documents de recherche 06-08, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
  • Handle: RePEc:eve:wpaper:06-08
    as

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    File URL: http://epee.univ-evry.fr/RePEc/2006/06-08.pdf
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    References listed on IDEAS

    as
    1. Serge Darolles & Gaëlle Le Fol, 2003. "Trading Volume and Arbitrage," Working Papers 2003-46, Center for Research in Economics and Statistics.
    2. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
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    6. Lo, Andrew W & Wang, Jiang, 2000. "Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory," Review of Financial Studies, Society for Financial Studies, pages 257-300.
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    14. James McCulloch, 2007. "Relative volume as a doubly stochastic binomial point process," Quantitative Finance, Taylor & Francis Journals, pages 55-62.
    15. Darrat, Ali F. & Rahman, Shafiqur & Zhong, Maosen, 2003. "Intraday trading volume and return volatility of the DJIA stocks: A note," Journal of Banking & Finance, Elsevier, vol. 27(10), pages 2035-2043, October.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Jedrzej Bialkowski & Serge Darolles & Gaëlle Le Fol, 2012. "Reducing the risk of VWAP orders execution - A new approach to modeling intra-day volume," Post-Print hal-01632822, HAL.
    2. Serge Darolles & Gaëlle Le Fol, 2003. "Trading Volume and Arbitrage," Working Papers 2003-46, Center for Research in Economics and Statistics.
    3. Ferriani, Fabrizio, 2010. "Informed and uninformed traders at work: evidence from the French market," MPRA Paper 24487, University Library of Munich, Germany.
    4. Serge Darolles & Gaëlle Le Fol & Gulten Mero, 2010. "When Market Illiquidity Generates Volumes," Working Papers halshs-00536046, HAL.
    5. Olivier Gu'eant & Guillaume Royer, 2013. "VWAP execution and guaranteed VWAP," Papers 1306.2832, arXiv.org, revised May 2014.
    6. Ito, R., 2016. "Spline-DCS for Forecasting Trade Volume in High-Frequency Finance," Cambridge Working Papers in Economics 1606, Faculty of Economics, University of Cambridge.
    7. Darolles, Serge & Fol, Gaëlle Le & Mero, Gulten, 2015. "Measuring the liquidity part of volume," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 92-105.
    8. Humphery-Jenner, M., 2011. "High Frequency Trading, Information, and Takeovers," Discussion Paper 2011-047, Tilburg University, Center for Economic Research.
    9. Francesco Calvori & Fabrizio Cipollini & Giampiero M. Gallo, 2014. "Go with the Flow: A GAS model for Predicting Intra-daily Volume Shares," Econometrics Working Papers Archive 2014_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    10. Alexander Malinowski & Martin Schlather & Zhengjun Zhang, 2016. "Intrinsically weighted means and non-ergodic marked point processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, pages 1-24.
    11. repec:eee:finlet:v:21:y:2017:i:c:p:249-258 is not listed on IDEAS
    12. Ye, Xunyu & Gao, Ping & Li, Handong, 2015. "Improving estimation of the fractionally differencing parameter in the SARFIMA model using tapered periodogram," Economic Modelling, Elsevier, vol. 46(C), pages 167-179.
    13. Yang, Yaxing & Ling, Shiqing, 2017. "Self-weighted LAD-based inference for heavy-tailed threshold autoregressive models," Journal of Econometrics, Elsevier, pages 368-381.
    14. Darolles, Serge & Le Fol, Gaëlle & Mero, Gulten, 2017. "Mixture of distribution hypothesis: Analyzing daily liquidity frictions and information flows," Journal of Econometrics, Elsevier, pages 367-383.
    15. Ye Xunyu & Yan Rui & Li Handong, 2014. "Forecasting trading volume in the Chinese stock market based on the dynamic VWAP," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, pages 1-20.
    16. Dutt, Tanuj & Humphery-Jenner, Mark, 2013. "Stock return volatility, operating performance and stock returns: International evidence on drivers of the ‘low volatility’ anomaly," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 999-1017.
    17. Alexandru Mandes, 2016. "Algorithmic and High-Frequency Trading Strategies: A Literature Review," MAGKS Papers on Economics 201625, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    18. Li, Yingying & Xie, Shangyu & Zheng, Xinghua, 2016. "Efficient estimation of integrated volatility incorporating trading information," Journal of Econometrics, Elsevier, pages 33-50.
    19. Fong, Kingsley Y.L. & Liu, Wai-Man, 2010. "Limit order revisions," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1873-1885, August.
    20. Alexander Malinowski & Martin Schlather & Zhengjun Zhang, 2016. "Intrinsically weighted means and non-ergodic marked point processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, pages 1-24.
    21. Enzo Busseti & Stephen Boyd, 2015. "Volume Weighted Average Price Optimal Execution," Papers 1509.08503, arXiv.org.
    22. Alexander Buryak & Ivan Guo, 2014. "Effective and simple VWAP option pricing model," Papers 1407.7315, arXiv.org.
    23. Humphery-Jenner, Mark L., 2011. "Optimal VWAP trading under noisy conditions," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2319-2329, September.
    24. Gulten Mero & Serge Darolles & Gaëlle Le Fol, 2015. "Financial Market Liquidity: Who Is Acting Strategically?," THEMA Working Papers 2015-14, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

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