Based on the formation and evolution of systemic risk, this study proposes high and low volatility spillover networks and explores the characteristics of the evolution of systemic risk in Chinese financial market, and identifies the source of risk accumulation and risk outbreak, as well as the corresponding contagion mechanisms. Moreover, a new multiscale decomposition method (MVMD) is used to decompose high and low volatility into different time frequency components (short-term and long-term), and the corresponding network is constructed. Upon comparing topological characteristics on each layer from system and individual levels, our results reveal that high and low volatility spillover networks have different network characteristics and evolution behaviors. At the individual level, bond market is always the largest risk net-receivers in the high and low volatility networks, while the futures market and the currency market are respectively risk net-emitters in the high and low volatility networks. Additionally, compared with high volatility network, the low volatility network has greater predictive ability for financial risk. Finally, frequency analysis demonstrates that high-low volatility networks have different spillover intensity and network structure at different time frequencies. The above findings are beneficial for policy makers and investors to formulate appropriate strategies in different evolution of systemic risk and time frequency.

The China-U.S. trade frictions and COVID-19 pandemic have caused unprecedentedly high economic policy uncertainty since 2017. To resist this high uncertainty, investors may prefer large stocks over small stocks, thereby damaging the size effect. To test this inference, we apply data from China to show that the size effect becomes insignificant after 2017. However, a significant size effect re-emerges among stocks with low valuations or low volatility, and this is positively associated with the increment of the economic policy uncertainty index. We also find that when uncertainty increases, institutional investors increase their holdings in small stocks before 2017, but hold more large stocks after 2017. Our findings consistently suggest that high policy uncertainty may change investors' preferences for firm size and cause the disappearance of the size effect, and only among stocks with low risk, size effects may show up due to low-risk small firms' similar function in resisting market uncertainty as large firms. Other mechanisms, such as the quality premium, unexpected profitability shock, shell value, or M&A option value, are not applicable in explaining the findings in China. Our study contributes to proposing a new mechanism for the time-variability of the size effect.

This study adopts the Markov-switching ARCH (hereafter SWARCH) model to examine the volatility nature and volatility linkages of four segmented Chinese stock indices (SHA, SZA, SHB, and SZB). Our empirical findings are consistent with the following notions. First, we find strong evidence of regime shift in the volatility of four segmented markets and SWARCH model appears to outperform standard GARCH family models. Second, although there are some common features of volatility switch in segmented markets, there exist a few difference: (i)compared with the A-share markets, B-share markets are more volatile and shift more frequently between high- and low-volatility states; (ii) B-share markets have longer stays at high volatility state than the A-share markets; (iii) the relative magnitude of the high volatility compared with that of the low volatility is much greater than the case in two A-share markets. Third, B-share markets are found to be more sensitive to international shocks, while the A-share markets seem immune to international spillovers of volatility. Finally, analyses of volatility spillover effect among the four stock markets indicate that the A-share markets play a dominant role in volatility in Chinese stock markets.