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Go with the Flow: A GAS model for Predicting Intra-daily Volume Shares

  • Francesco Calvori

    ()

    (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze)

  • Fabrizio Cipollini

    ()

    (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze)

  • Giampiero M. Gallo

    ()

    (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze)

The Volume Weighted Average Price (VWAP) mixes volumes and prices at intra-daily intervals and is a benchmark measure frequently used to evaluate a trader's performance. Under suitable assumptions, splitting a daily order according to ex-ante volume predictions is a good strategy to replicate the VWAP. To bypass possible problems generated by local trends in volumes, we propose a novel Generalized Autoregressive Score (GAS) model for predicting volume shares (relative to the daily total), inspired by the empirical regularities of the observed series (intra-daily periodicity pattern, residual serial dependence). An application to six NYSE tickers confirms the suitability of the model proposed in capturing the features of intra-daily dynamics of volume shares.

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File URL: http://local.disia.unifi.it/wp_disia/2014/wp_disia_2014_01.pdf
File Function: Revision 2014-02
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Paper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" in its series Econometrics Working Papers Archive with number 2014_01.

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Length: 21 pages
Date of creation: Feb 2014
Date of revision: Feb 2014
Handle: RePEc:fir:econom:wp2014_01
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  1. Serge Darolles & Gaëlle Le Fol, 2003. "Trading Volume and Arbitrage," Working Papers 2003-46, Centre de Recherche en Economie et Statistique.
  2. James McCulloch & Vladimir Kazakov, 2007. "Optimal VWAP Trading Strategy and Relative Volume," Research Paper Series 201, Quantitative Finance Research Centre, University of Technology, Sydney.
  3. Brownlees, C.T. & Gallo, G.M., 2006. "Financial econometric analysis at ultra-high frequency: Data handling concerns," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2232-2245, December.
  4. 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.
  5. Nikolaus Hautsch & Ruihong Huang, 2009. "The Market Impact of a Limit Order," SFB 649 Discussion Papers SFB649DP2009-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  6. Dufour, Alfonso & Engle, Robert F, 1999. "Time and the Price Impact of a Trade," University of California at San Diego, Economics Working Paper Series qt62c0h04j, Department of Economics, UC San Diego.
  7. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Intra-daily Volume Modeling and Prediction for Algorithmic Trading," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(3), pages 489-518, Summer.
  8. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  9. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
  10. Newey, Whitney K, 1985. "Maximum Likelihood Specification Testing and Conditional Moment Tests," Econometrica, Econometric Society, vol. 53(5), pages 1047-70, September.
  11. Konishi, Hizuru, 2002. "Optimal slice of a VWAP trade," Journal of Financial Markets, Elsevier, vol. 5(2), pages 197-221, April.
  12. Berkowitz, Stephen A & Logue, Dennis E & Noser, Eugene A, Jr, 1988. " The Total Cost of Transactions on the NYSE," Journal of Finance, American Finance Association, vol. 43(1), pages 97-112, March.
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