  Recently the statistical characterizations of financial markets based on
physics concepts and methods attract considerable attentions. We used two
possible procedures of analyzing multifractal properties of a time series. The
first one uses the continuous wavelet transform and extracts scaling exponents
from the wavelet transform amplitudes over all scales. The second method is the
multifractal version of the detrended fluctuation analysis method (MF-DFA). The
multifractality of a time series we analysed by means of the difference of
values singularity stregth as a suitable way to characterise multifractality.
Singularity spectrum calculated from daily returns using a sliding 1000 day
time window in discrete steps of 1-10 days. We discovered that changes in the
multifractal spectrum display distinctive pattern around significant
"drawdowns". Finally, we discuss applications to the construction of crushes
precursors at the financial markets.
