Abstract
A group of stock markets can be treated as a complex system. We tried to find the financial market crisis by constructing a global 24 stock market network while using detrended cross-correlation analysis. The community structures by the Girvan-Newman method are observed and other network properties, such as the average degree, clustering coefficient, efficiency, and modularity, are quantified. The criterion of correlation between any two markets on the detrended cross-correlation analysis was considered to be 0.7. We used the return (rt) and volatility (|rt|) time series for the periods of 1, 4, 10, and 20-year of composite stock price indices during 1997–2016. Europe (France, Germany, Netherland, UK), USA (USA1, USA2, USA3, USA4) and Oceania (Australia1, Australia2) have been confirmed to make a solid community. This approach also detected the signal of financial crisis, such as Asian liquidity crisis in 1997, world-wide dot-com bubble collapse in 2001, the global financial crisis triggered by the USA in 2008, European sovereign debt crisis in 2010, and the Chinese stock price plunge in 2015 by capturing the local maxima of average degree and efficiency.
Highlights
Time series analysis and complex networks in the field of statistical physics are the main research areas for decades
Europe (France, Germany, Netherland, UK), USA (USA1, USA2, USA3, USA4) and Oceania (Australia1, Australia2) have been confirmed to make a solid community. This approach detected the signal of financial crisis, such as Asian liquidity crisis in 1997, world-wide dot-com bubble collapse in 2001, the global financial crisis triggered by the USA in 2008, European sovereign debt crisis in 2010, and the Chinese stock price plunge in 2015 by capturing the local maxima of average degree and efficiency
We looked for community structures and other network properties of return and volatility of global 24 stock market networks
Summary
Time series analysis and complex networks in the field of statistical physics are the main research areas for decades. Wu et al [15] analyzed the network community structure of a stock market using the Girvan-Newman method for 180-index data registered in the SSE (Shanghai Stock Exchange) Silva et al [16] built a network using the Pearson correlation coefficient for 348 stocks in the New York Stock Exchange They created a network that reflected time evolution, and analyzed the network structure before, after, and during the Black Monday crisis. We studied the DCCA method, traditional complex network analysis [5,23], and Girvan-Newman method to detect the optimal community of global 24 stock markets. The derived correlation is used to determine whether to connect the links between two nodes in network analysis
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