The High Frequency requires an appropriate way to treat the data?
If yes, which is the appropriate methodology to do this?
Have a look at these papers :
1. http://www.unc.edu/~eghysels/papers/GCMFVARM_v13.pdf
2. https://economics.sas.upenn.edu/system/files/event_papers/E0.pdf
3. http://www.aoni.waseda.jp/motegi/GCMFVARM_R1_v7.pdf
The papers cited by Osman would be very helpful.
However, high frequency data can be treated just the same way as regular time series data. Its just that the size of the data may be large.
Penna,
you may look at the paper in the link below. You can just use the same procedure as in the paper. But it is important to check the direction of causality. I hope this helps.
Regards,
http://www.iiste.org/Journals/index.php/MTM/article/view/9678/9903
Have you heard about convergent cross mapping(CCM)[1]? But , if your data are purely stochastic than it might give you any result.. but , some data might appear as stochastic , but it might be the case that the data generteting process is deterministic dynamic … in that cause granger causality is not reliable , and CCM is a really good solution.
1. Sugihara, G., May, R., Ye, H., Hsieh, C. H., Deyle, E., Fogarty, M., & Munch, S. (2012). Detecting causality in complex ecosystems. science, 338(6106), 496-500.