  The definition of time is still an open question when one deals with high
frequency time series. If time is simply the calendar time, prices can be
modeled as continuous random processes and values resulting from transactions
or given quotes are discrete samples of this underlying dynamics. On the
contrary, if one takes the business time point of view, price dynamics is a
discrete random process, and time is simply the ordering according which prices
are quoted in the market. In this paper we suggest that the business time
approach is perhaps a better way of modeling price dynamics than calendar time.
This conclusion comes out from testing probability densities and conditional
variances predicted by the two models against the experimental ones. The data
set we use contains the DEM/USD exchange quotes provided to us by Olsen &
Associates during a period of one year from January to December 1998. In this
period 1,620,843 quotes entries in the EFX system were recorded.
