Multifractal Properties of the Ukraine Stock Market

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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.
LaTeX, 9 pages, 6 eps figure; Proc. APFA5 Conference, Torino, 2006

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Data Analysis, Statistics and Probability, Statistical Finance

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http://salesiana.dossiersoluciones.com/handle/123456789/209105

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/physics/0608009
 http://arxiv.org/abs/physics/0608009

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arXiv