Abstract:
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 αmax and αmin as a suitable way to
characterise multifractality. Singularity spectrum calculated from daily re-
turns 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.
Description:
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