Economics at your fingertips  

Intra-daily Volume Modeling and Prediction for Algorithmic Trading

Christian Brownlees (), Fabrizio Cipollini () and Giampiero Gallo ()

Journal of Financial Econometrics, 2011, vol. 9, issue 3, 489-518

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:, Oxford University Press.

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (27) Track citations by RSS feed

Downloads: (external link) (application/pdf)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Intra-daily Volume Modeling and Prediction for Algorithmic Trading (2009) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

Access Statistics for this article

Journal of Financial Econometrics is currently edited by RenÈ Garcia and Eric Renault

More articles in Journal of Financial Econometrics from Society for Financial Econometrics Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().

Page updated 2020-11-15
Handle: RePEc:oup:jfinec:v:9:y:2011:i:3:p:489-518