Stock price network autoregressive model with application to stock market turbulence

In this article, the authors develop a Stock Price Network Autoregressive Model (SPNAR) to probe the behavior of the log-return based network of the Chinese stock market. We consider 105 companies of Shanghai and Shenzhen stock market, CSI300, during the steep sell-off in 2015–2016. This model is based on three effects of previous time effect, market effect, and independent noise effect. The results show that the accuracy and performance of this model are more than some time series models like Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Vector Autoregressive (VAR) models. Furthermore, the parameter estimation in SPNAR model is more convenient and feasible than time series models as mentioned earlier. Moreover, In this article, the characteristics of three various periods, pre-turbulence, turbulence, and post-turbulence are analyzed, and findings show there is a significant difference between turbulence period with other periods in topological structure and the behavior of the networks.