Intra-daily Volume Modeling and Prediction for Algorithmic Trading

Christian T. Brownlees (), Fabrizio Cipollini () and Giampiero M. Gallo ()
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Christian T. Brownlees: Stern School of Business, New York University, http://pages.stern.nyu.edu/~cbrownle/

Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti"

Abstract: The explosion of algorithmic trading has been one of the most prominent 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 a loss functions for the evaluation of proportions forecasts which retains both an operational and information theoretic interpretation. An empirical application on a set of widely traded index ETFs shows that the proposed methodology is able to significantly outperform common forecasting methods and delivers significantly more precise predictions for VWAP trading.

Keywords: Traded volume; VWAP; MEM; High Frequency Data; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C51 C52 (search for similar items in EconPapers)
Date: 2009-02

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Persistent link: http://EconPapers.repec.org/RePEc:fir:econom:wp2009_01

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