Intra-daily Volume Modeling and Prediction for Algorithmic Trading
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.
|Date of creation:||Feb 2009|
|Date of revision:|
|Contact details of provider:|| Postal: Viale G.B. Morgagni, 59 - I-50134 Firenze - Italy|
Phone: +39 055 2751500
Fax: +39 055 4223560
Web page: http://www.disia.unifi.it/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:fir:econom:wp2009_01. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Francesco Calvori)
If references are entirely missing, you can add them using this form.