This paper investigates the impact of economic policy uncertainty (EPU) on the crash risk of US stock market during the COVID-19 pandemic. To this end, we use the GARCH-S (GARCH with skewness) model to estimate daily skewness as a proxy for the stock market crash risk. The empirical results show the significantly negative correlation between EPU and stock market crash risk, indicating the aggravation of EPU increase the crash risk. Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the epidemic.

One of the main differences between sentiment and infectious diseases is that the former one has two opposite infectious states: positive (optimistic) and negative (pessimistic), while the latter one has not. In this paper, based on the SISa model, we consider this issue and propose a new model of sentiment contagion called the SOSa-SPSa model. The results of both numerical and agent-based simulations show that our model could explain the process of sentiment contagion better than that of Hill et al. (2010). Further analysis shows that both the numbers of optimistic and pessimistic individuals will increase with the probability of spontaneity or contagion and decrease with the probability of recovery. Potential applications of this model in financial market have also been discussed.

In this article, by using the mixed-frequency data sampling (MIDAS) model, we investigate whether economic policy uncertainty (EPU) can predict financial stress. Our estimation results show that EPU has a significant positive effect on the future financial stress, indicating that EPU is a determinant of financial stress. Moreover, the out-of-sample prediction results show that the MIDAS model performs better than the traditional time-series OLS model.

Current research on the impact of social interaction on the stock market participation only involves the traditional way of social interaction, and this paper further investigates the modern social interaction effects on the stock market participation and its activeness. The sample containing 150 Chinese counties is selected, and we apply grouping analysis and linear regression to conclude that social interaction has positive influence on the stock market participation and its activeness. Both traditional and modern social interaction ways affect the stock market participation and its activeness to the similar extent, so modern social interaction is of the same importance. Controlling for the respondents’ age, wealth, and education level, the above conclusion still holds.