  Properties of low-variability periods in the time series are analysed. The
theoretical approach is used to show the relationship between the multi-scaling
of low-variability periods and multi-affinity of the time series. It is shown
that this technically simple method is capable of reveling more details about
time-series than the traditional multi-affine analysis. We have applied this
scaling analysis to financial time series: a number of daily currency and stock
index time series. The results show a good scaling behaviour for different
model parameters. The analysis of high-frequency USD-EUR exchange rate data
confirmed the theoretical expectations.
