Modeling High-Frequency Foreign Exchange Data Dynamics
AbstractThis paper shows that high-frequency, irregularly spaced, foreign exchange (FX) data can generate nonnormality, conditional heteroskedasticity, and leptokurtosis when aggregated into fixed-interval calendar time, even when these features are absent in the original DGP. Furthermore, we introduce a new approach to modeling these high-frequency irregularly spaced data based on the Poisson regression model. The new model is called the autoregressive conditional intensity model and it has the advantage of being simple and of maintaining the calendar timescale. To illustrate the virtues of this approach, we examine a classical issue in FX microstructure: the variation in information content as a function of fluctuations in the intensity of activity levels.
Download InfoIf 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.
Bibliographic InfoArticle provided by Cambridge University Press in its journal Macroeconomic Dynamics.
Volume (Year): 7 (2003)
Issue (Month): 04 (September)
Contact details of provider:
Postal: The Edinburgh Building, Shaftesbury Road, Cambridge CB2 2RU UK
Fax: +44 (0)1223 325150
Web page: http://journals.cambridge.org/jid_MDYProvider-Email:firstname.lastname@example.org
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Helmut Herwartz, 2006. "Econometric analysis of high frequency data," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 89-104, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Keith Waters).
If references are entirely missing, you can add them using this form.