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Intra-daily Volume Modeling and Prediction for Algorithmic Trading

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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.

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Bibliographic Info

Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

Volume (Year): 9 (2011)
Issue (Month): 3 (Summer)
Pages: 489-518

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Handle: RePEc:oup:jfinec:v:9:y:2011:i:3:p:489-518

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Cited by:
  1. Hautsch, Nikolaus & Malec, Peter & Schienle, Melanie, 2011. "Capturing the zero: A new class of zero-augmented distributions and multiplicative error processes," CFS Working Paper Series, Center for Financial Studies (CFS) 2011/25, Center for Financial Studies (CFS).
  2. 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, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" 2014_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
  3. Wolfgang Karl Härdle & Nikolaus Hautsch & Andrija Mihoci, 2012. "Local Adaptive Multiplicative Error Models for High-Frequency Forecasts," SFB 649 Discussion Papers, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany SFB649DP2012-031, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  4. Malec, Peter & Schienle, Melanie, 2014. "Nonparametric kernel density estimation near the boundary," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 72(C), pages 57-76.
  5. 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, Elsevier, vol. 37(3), pages 999-1017.
  6. Ito, Ryoko, 2013. "Modeling dynamic diurnal patterns in high frequency financial data," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 1315, Faculty of Economics, University of Cambridge.
  7. Taras Bodnar & Nikolaus Hautsch, 2012. "Copula-Based Dynamic Conditional Correlation Multiplicative Error Processes," SFB 649 Discussion Papers, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany SFB649DP2012-044, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  8. Ferriani, Fabrizio, 2010. "Informed and uninformed traders at work: evidence from the French market," MPRA Paper 24487, University Library of Munich, Germany.
  9. E. Otranto, 2012. "Spillover Effects in the Volatility of Financial Markets," Working Paper CRENoS, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia 201217, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

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