Self-Organized Criticality and Stock Market Dynamics: an Empirical Study

Abstract

The Stock Market is a complex self-interacting system, characterized by an intermittent behaviour. Periods of high activity alternate with periods of relative calm. In the present work we investigate empirically about the possibility that the market is in a self-organized critical state (SOC). A wavelet transform method is used in order to separate high activity periods, related to the avalanches of sandpile models, from quiescent. A statistical analysis of the filtered data show a power law behaviour in the avalanche size, duration and laminar times. The memory process, implied by the power law distribution, of the laminar times is not consistent with classical conservative models for self-organized criticality. We argue that a ``near-SOC'' state or a time dependence in the driver, which may be chaotic, can explain this behaviour.

16 pages, 13 figures. In press: Physica A

References in corpus (2)

• Stochastic Cellular Automata Model for Stock Market Dynamics
• To what extent can dynamical models describe statistical features of turbulent flows?

Cited by in corpus (9)

• Self-Similar Log-Periodic Structures in Western Stock Markets from 2000
• Scale-free avalanche dynamics in the stock market
• Critical bursts in filtration
• Influence of Local Interactions in the Bak-Sneppen Model and Economic Applications
• Some Properties of Sandpile Models as Prototype of Self-Organized Critical Systems
• Scale-free avalanches in the multifractal random walk
• Statistical Properties of Fluctuations: A Method to Check Market Behavior
• Theoretical Models of Self-Organized Criticality (SOC) Systems
• Spectral Analysis of Dow Jones Index and Comparison with Model Predicted Cycles During 1900-2005