Abstract
In this paper, we investigated the effectiveness of price limit on stock market with the correlation study and complex network technology. We proposed a time-migrated DCCA cross-correlation coefficient which is beneficial to detect the asynchronous correlations of nonstationary time series. The stock market network is constructed with the threshold method based on time-migrated DCCA. The effectiveness of the price limit during the stock market crash period is studied based on the time-migrated DCCA stock market network. The results indicate that the time-migrated DCCA ensures more relevant results than the equal-time DCCA method. An interesting finding is that the price limit has different effects on the stock market network at different stages of dynamic evolution. Market stabilization will be lowered and the systemic risk will be increased if the price limit is enhanced. Such studies are relevant for a better understanding of the stock market and have a significant contribution to the stock market in reality.
Highlights
It is believed that a number of systems could be described by complex networks, including traffic systems, ecological systems, and financial systems. e applications of complex networks have provided a new perspective for studying the mechanisms of these systems
We attempt to provide some insight into the effectiveness of price limit by simulating the dynamic evolution of timemigrated Detrended Cross-correlation Analysis (DCCA) stock network model
We investigated the effectiveness of price limit on stock market based on the correlation study and complex network technology
Summary
It is believed that a number of systems could be described by complex networks, including traffic systems, ecological systems, and financial systems. e applications of complex networks have provided a new perspective for studying the mechanisms of these systems. Previous studies suggest that a stock market network could be established based on the price correlations. People provided many effective complex network construction methods such as minimum cost spanning tree (MST), planar maximally filtered graph (PMFG), and correlation threshold method. Mantegna used the Pearson correlation and MST method to build a stock network and revealed the general hierarchical structure of the market [3]. Bonanno et al used the method and found that stock market networks present different hierarchical structures as the time horizon changes [4]. Tumminello et al used the correlation coefficient between stock price dynamics time series and the PMFG method to generate stock networks [5]
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