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Intra-daily Volume Modeling and Prediction for Algorithmic Trading

Author

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  • Christian T. Brownlees
  • Fabrizio Cipollini
  • Giampiero M. Gallo

Abstract

The explosion of algorithmic trading has been one of the most pro-minent recent trends in the financial industry. Algorithmic trading consists of automated trading strategies that attempt to minimize transaction costs by optimally placing orders. The key ingredient of many of these strategies are intra-daily volume proportions forecasts. This work proposes a dynamic model for intra-daily volumes that captures salient features of the series such as time series dependence, intra-daily periodicity and volume asymmetry. Moreover, we introduce loss functions for the evaluation of proportion forecasts which retains both an operational and information theoretic interpretation. An empirical application on a set of widely traded index Exchange Traded Funds shows that the proposed methodology is able to significantly outperform common forecasting methods and delivers more precise predictions for Volume Weighted Average Price trading. (JEL: C22, C51, C53, G12) Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:jfinec:v:9:y:2011:i:3:p:489-518
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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