dorsal/arxiv
View SchemaMultifractal Properties of the Ukraine Stock Market
| Authors | A. Ganchuk, V. Derbentsev, V. Soloviev |
|---|---|
| Categories | |
| ArXiv ID | physics/0608009 |
| URL | https://arxiv.org/abs/physics/0608009 |
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 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.
{
"annotation_id": "cc1dbf99-4e80-443c-828a-489317c3f4b0",
"date_created": "2026-03-02T18:01:11.109000Z",
"date_modified": "2026-03-02T18:01:11.109000Z",
"file_hash": "cee726b5019c8bd2d4f3eeb041d50603513bfdf67d0f542d068e77ebbb181ce7",
"private": false,
"record": {
"abstract": "Recently the statistical characterizations of financial markets based on\nphysics concepts and methods attract considerable attentions. We used two\npossible procedures of analyzing multifractal properties of a time series. The\nfirst one uses the continuous wavelet transform and extracts scaling exponents\nfrom the wavelet transform amplitudes over all scales. The second method is the\nmultifractal version of the detrended fluctuation analysis method (MF-DFA). The\nmultifractality of a time series we analysed by means of the difference of\nvalues singularity stregth as a suitable way to characterise multifractality.\nSingularity spectrum calculated from daily returns using a sliding 1000 day\ntime window in discrete steps of 1-10 days. We discovered that changes in the\nmultifractal spectrum display distinctive pattern around significant\n\"drawdowns\". Finally, we discuss applications to the construction of crushes\nprecursors at the financial markets.",
"arxiv_id": "physics/0608009",
"authors": [
"A. Ganchuk",
"V. Derbentsev",
"V. Soloviev"
],
"categories": [
"physics.data-an",
"q-fin.ST"
],
"title": "Multifractal Properties of the Ukraine Stock Market",
"url": "https://arxiv.org/abs/physics/0608009"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "5717dcd0-c222-42e0-89bd-60605fc0491e",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}