Advanced Search
MyIDEAS: Login to save this paper or follow this series

Ensemble properties of high frequency data and intraday trading rules


Author Info

  • Fulvio Baldovin
  • Francesco Camana
  • Massimiliano Caporin
  • Michele Caraglio
  • Attilio L. Stella


Regarding the intraday sequence of high frequency returns of the S&P index as daily realizations of a given stochastic process, we first demonstrate that the scaling properties of the aggregated return distribution can be employed to define a martingale stochastic model which consistently replicates conditioned expectations of the S&P 500 high frequency data in the morning of each trading day. Then, a more general formulation of the above scaling properties allows to extend the model to the afternoon trading session. We finally outline an application in which conditioned forecasting is used to implement a trend-following trading strategy capable of exploiting linear correlations present in the S&P dataset and absent in the model. Trading signals are model-based and not derived from chartist criteria. In-sample and out-of-sample tests indicate that the model-based trading strategy performs better than a benchmark one established on an asymmetric GARCH process, and show the existence of small arbitrage opportunities. We remark that in the absence of linear correlations the trading profit would vanish and discuss why the trading strategy is potentially interesting to hedge volatility risk for S&P index-based products.

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:
File Function: Latest version
Download Restriction: no

Bibliographic Info

Paper provided by in its series Papers with number 1202.2447.

as in new window
Date of creation: Feb 2012
Date of revision: Jul 2013
Handle: RePEc:arx:papers:1202.2447

Contact details of provider:
Web page:

Related research


This paper has been announced in the following NEP Reports:


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


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

Cited by:
  1. Fulvio Baldovin & Francesco Camana & Michele Caraglio & Attilio L. Stella & Marco Zamparo, 2012. "Aftershock prediction for high-frequency financial markets' dynamics," Papers 1203.5893,, revised Jul 2012.
  2. Fulvio Baldovin & Massimiliano Caporin & Michele Caraglio & Attilio Stella & Marco Zamparo, 2013. "Option pricing with non-Gaussian scaling and infinite-state switching volatility," Papers 1307.6322,, revised May 2014.


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


Access and download statistics


When requesting a correction, please mention this item's handle: RePEc:arx:papers:1202.2447. 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: (arXiv administrators).

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.