Advanced Search
MyIDEAS: Login

Intra-daily Volume Modeling and Prediction for Algorithmic Trading

Contents:

Author Info

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.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://local.disia.unifi.it/ricerca/pubblicazioni/working_papers/2009/wp2009_01.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" in its series Econometrics Working Papers Archive with number wp2009_01.

as in new window
Length: 27
Date of creation: Feb 2009
Date of revision:
Handle: RePEc:fir:econom:wp2009_01

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

Related research

Keywords: Traded volume; VWAP; MEM; High Frequency Data; Forecasting;

Other versions of this item:

Find related papers by JEL classification:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

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

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

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 you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.