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Forecasting intraday volume: Comparison of two early models

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  • Szűcs, Balázs Árpád

Abstract

There are few intraday volume forecasting models in the literature, and they do not reflect on each other regarding forecast performance. This paper compares two models that are often referenced: the model of Bialkowski et al. (2008) to that of Brownlees et al. (2011) using intraday data that covers 11 years of 33 NYSE and NASDAQ shares. The former is found to produce more accurate forecasts, while its estimation is faster by several orders of magnitude.

Suggested Citation

  • Szűcs, Balázs Árpád, 2017. "Forecasting intraday volume: Comparison of two early models," Finance Research Letters, Elsevier, vol. 21(C), pages 249-258.
    • Handle: RePEc:eee:finlet:v:21:y:2017:i:c:p:249-258
    DOI: 10.1016/j.frl.2016.11.018
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    References listed on IDEAS

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    1. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Intra-daily Volume Modeling and Prediction for Algorithmic Trading," Journal of Financial Econometrics, Oxford University Press, vol. 9(3), pages 489-518, Summer.
    2. 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.
    3. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    4. Humphery-Jenner, Mark L., 2011. "Optimal VWAP trading under noisy conditions," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2319-2329, September.
    5. Frömmel, Michael & Lampaert, Kevin, 2016. "Does frequency matter for intraday technical trading?," Finance Research Letters, Elsevier, vol. 18(C), pages 177-183.
    6. Kissell, Robert & Glantz, Morton & Malamut, Roberto, 2004. "A practical framework for estimating transaction costs and developing optimal trading strategies to achieve best execution," Finance Research Letters, Elsevier, vol. 1(1), pages 35-46, March.
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    Cited by:

    1. Roman Huptas, 2019. "Point forecasting of intraday volume using Bayesian autoregressive conditional volume models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(4), pages 293-310, July.

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