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MODELING HIGH-FREQUENCY FOREIGN EXCHANGE DATA DYNAMICS

2003; Cambridge University Press; Volume: 7; Issue: 04 Linguagem: Inglês

10.1017/s136510050202014x

1469-8056

Óscar Jordá, Massimiliano Marcellino,

Theoretical and Computational Physics

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.