This study applies the generalized dynamic factor model (GDFM), TVPVAR-DY framework, and pattern causality to investigate spillover effect from international commodity idiosyncratic volatility co-movements to China's financial market risk, as well as the impact of a series of macroeconomic factors on such spillover effect. The empirical results indicate that the idiosyncratic volatility co-movements of energy, industrial metals, precious metals, soft commodities, and agricultural products all have significant spillover effects on China's financial market risk. The influence of commodity idiosyncratic co-movements on China’s financial market risk is relatively stable under normal economic conditions but intensifies significantly during periods of deteriorating economic fundamentals. Macroeconomic factors such as international capital flows, investor sentiment, geopolitical risks, economic conditions, and international freight rates predominantly exhibit a positive causal effect on the dynamic spillover effect.

As China’s Zero-COVID policy has come to an end and travel restrictions have been removed, the country’s mobility patterns are very likely to become more heterogeneous than during the pandemic. Human mobility is a key mechanism through which economic activities emerge and viruses spread. It can bring both advantages and challenges to cities with different characteristics. This paper investigates intra-city mobility trajectories of 368 Chinese cities within a non-linear time-varying latent factor framework to uncover the evolution of heterogeneity in local travel behavior amidst that China has been approaching the turning point of the post-pandemic new normal. To this end, we compiled a novel panel on a weekly basis, using the latest Baidu Mobility Data and the risk-level data released by the State Council of the People’s Republic of China. We further examine the effects of exposure to high COVID-19 risk in the city on commuting behavior between May 17, 2021 and June 26, 2022. Our results provide stylized facts on stratified local travel across China: first, the 368 cities can be categorized into six clusters based on their mobility dynamics, and second, the gaps in intra-city mobility tend to narrow within each cluster but widen between different clusters. Moreover, exposure to high COVID-19 risk has a stronger impact on home-workplace commuting rates than on dining-, leisure, and recreational travel rates, persistently dampening commuting behavior. In addition, divisions in intra-city travel strength and commuting behavior between western regions and the rest of China are evident. In sum, this paper suggests that the daily life and economic activities which depend heavily on human mobility are recovering at different rates across China.