This paper shows that high-frequency, irregularly-spaced, FX data can generate non-normality, conditional heteroskedasticity, and leptokurtosis when aggregated into ¯xed-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.
Modeling High-Frequency Foreign Exchange Data Dynamics
MARCELLINO, MASSIMILIANO;
2003
Abstract
This paper shows that high-frequency, irregularly-spaced, FX data can generate non-normality, conditional heteroskedasticity, and leptokurtosis when aggregated into ¯xed-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.
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