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MODELING HIGH-FREQUENCY FOREIGN EXCHANGE DATA DYNAMICS

Published online by Cambridge University Press:  01 August 2003

ÒSCAR JORDÀ  and
MASSIMILIANO MARCELLINO
ÒSCAR JORDÀ
Affiliation:
University of California at Davis
MASSIMILIANO MARCELLINO
Affiliation:
IGIER—Università Bocconi and CEPR
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Abstract

This 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.

Keywords

Time AggregationIrregularly Spaced High-Frequency DataDependent Point Process
Type
Articles
Information
Macroeconomic Dynamics , Volume 7 , Issue 4 , September 2003 , pp. 618 - 635
Copyright
© 2003 Cambridge University Press

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