  We perform wavelet decomposition of high frequency financial time series into
large and small time scale components. Taking the FTSE100 index as a case
study, and working with the Haar basis, it turns out that the small scale
component defined by most ($\simeq$ 99.6%) of the wavelet coefficients can be
neglected for the purpose of option premium evaluation. The relevance of the
hugely compressed information provided by low-pass wavelet-filtering is related
to the fact that the non-gaussian statistical structure of the original
financial time series is essentially preserved for expiration times which are
larger than just one trading day.
