  We develop a framework especially suited to the autocorrelation properties
observed in financial times series, by borrowing from the physical picture of
turbulence. The success of our approach as applied to high frequency foreign
exchange data is demonstrated by the overlap of the curves in Figure (1), since
we are able to provide an analytical derivation of the relative sizes of the
quantities depicted. These quantities include departures from Gaussian
probability density functions and various two and three-point autocorrelation
functions.
