Non-criticality of interaction network over system's crises: A percolation analysis
Amir Hossein Shirazi1, Abbas Ali Saberi2,3,4, Ali Hosseiny5,6
1Department of Physics, Shahid Beheshti University, G.C., Evin, Tehran, 19839, Iran. amir.h.shirazi@gmail.com.
Scientific Reports
|November 22, 2017
Summary
This study reveals how financial market interaction networks change during crises. Unlike normal periods, crisis networks exhibit distinct behaviors, deviating from standard random network models.
Area of Science:
- Complex Systems Science
- Network Science
- Financial Market Analysis
Background:
- Extracting interaction networks from multivariate time-series data is crucial for understanding complex systems.
- Previous research primarily focused on pairwise relationships, neglecting aggregated system behavior.
Purpose of the Study:
- To explore the potential of interaction network extraction for understanding aggregated system behavior.
- To connect network dynamics with percolation theory concepts.
- To analyze financial market interaction networks during crisis and non-crisis periods.
Main Methods:
- Extraction of dynamical interaction networks from multivariate time-series data of financial indices.
- Construction of weighted networks based on correlations.
- Comparison of network properties with percolation theory models, specifically Erdős-Rényi random networks.
- Analysis of network behavior during financial crises versus stable periods.
Main Results:
- Interaction networks in financial markets resemble critical Erdős-Rényi random networks during stable periods.
- Close to financial crises, network behavior deviates significantly from random network models.
- This deviation is scale-dependent and not solely due to increased correlations during crises.
Conclusions:
- Interaction network analysis, informed by percolation theory, offers insights into financial market dynamics.
- Financial crises represent distinct states of network organization compared to normal market conditions.
- The findings highlight the importance of considering aggregated network behavior for crisis prediction and understanding.
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