dorsal/arxiv
View SchemaScale-free avalanche dynamics in the stock market
| Authors | M. Bartolozzi, D. B. Leinweber, A. W. Thomas |
|---|---|
| Categories | |
| ArXiv ID | physics/0601171 |
| URL | https://arxiv.org/abs/physics/0601171 |
| DOI | 10.1016/j.physa.2006.04.024 |
Abstract
Self-organized criticality has been claimed to play an important role in many natural and social systems. In the present work we empirically investigate the relevance of this theory to stock-market dynamics. Avalanches in stock-market indices are identified using a multi-scale wavelet-filtering analysis designed to remove Gaussian noise from the index. Here new methods are developed to identify the optimal filtering parameters which maximize the noise removal. The filtered time series is reconstructed and compared with the original time series. A statistical analysis of both high-frequency Nasdaq E-mini Futures and daily Dow Jones data is performed. The results of this new analysis confirm earlier results revealing a robust power law behaviour in the probability distribution function of the sizes, duration and laminar times between avalanches. This power law behavior holds the potential to be established as a stylized fact of stock market indices in general. While the memory process, implied by the power law distribution of the laminar times, is not consistent with classical models for self-organized criticality, we note that a power-law distribution of the laminar times cannot be used to rule out self-organized critical behaviour.
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"abstract": "Self-organized criticality has been claimed to play an important role in many\nnatural and social systems. In the present work we empirically investigate the\nrelevance of this theory to stock-market dynamics. Avalanches in stock-market\nindices are identified using a multi-scale wavelet-filtering analysis designed\nto remove Gaussian noise from the index. Here new methods are developed to\nidentify the optimal filtering parameters which maximize the noise removal. The\nfiltered time series is reconstructed and compared with the original time\nseries. A statistical analysis of both high-frequency Nasdaq E-mini Futures and\ndaily Dow Jones data is performed. The results of this new analysis confirm\nearlier results revealing a robust power law behaviour in the probability\ndistribution function of the sizes, duration and laminar times between\navalanches. This power law behavior holds the potential to be established as a\nstylized fact of stock market indices in general. While the memory process,\nimplied by the power law distribution of the laminar times, is not consistent\nwith classical models for self-organized criticality, we note that a power-law\ndistribution of the laminar times cannot be used to rule out self-organized\ncritical behaviour.",
"arxiv_id": "physics/0601171",
"authors": [
"M. Bartolozzi",
"D. B. Leinweber",
"A. W. Thomas"
],
"categories": [
"physics.soc-ph",
"physics.data-an",
"q-fin.ST"
],
"doi": "10.1016/j.physa.2006.04.024",
"title": "Scale-free avalanche dynamics in the stock market",
"url": "https://arxiv.org/abs/physics/0601171"
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