Modeling High-Frequency FX Data Dynamics∗

This paper shows that high-frequency, irregularly-spaced, FX data can generate non-normality, conditional heteroskedasticity, and leptokurtosis when aggregated into fixed-interval, calendar time even when these features are absent in the original D.G.P. 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 (ACI) model and it has the advantage of being simple and of maintaining the calendar time scale. 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. • JEL Classification Codes: C43, C22, F31