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.

This paper measures the “greenium” in China’s stock markets with data during 2011-2020. We find that the green stocks outperform the brown ones in China and public climate concern brings the greenium. Based on the phenomenon, with panel regression, we furtherly figure out that the firms’ stock returns are positively correlated with their ESG rating, and public climate concern strengthens the relationship, which suggests that China’s stock investors behavioral bias contributes to greenium.

This study investigates the relationship between directors’ foreign experience and corporate environmental disclosure in Chinese listed firms from 2009 to 2017. The research shows that directors with foreign experience have a positive and significant impact on corporate environmental disclosure. This effect is more pronounced in nonstate-owned enterprises, where directors have greater influence over managerial decisions. Additionally, the study suggests that in industries with high energy consumption, high pollution, or overcapacity, the positive effect can be further enhanced by having at least three directors with foreign experience or foreign experience members in the audit committee. The impact of experiential diversity on environmental disclosure is greater than that of board gender and independence diversity. The findings suggest that policymakers and firms prioritize the recruitment of directors with diverse experiences to improve their environmental disclosure practices.

From an industry perspective, this paper applies the quantile regression to investigate the impact of investor sentiment (IS) and China’s/U.S. geopolitical risks (GPR) on Chinese stock market volatility. Considering the structural break of the stock market for theperiod2003/02-2021/10, we find that the impact of geopolitical risk on stock market volatility is highly heterogeneous, and its significance mostly appears in the upper and lower tails. At the market level, China’s and U.S. GPR/IS and their interaction effects have no significant impact on China’s stock market volatility. However, there has an asymmetric dependence between China’s and U.S. GPR/IS and stock market volatility, and the dependence structure is changing. At the industry level, the current and lagging effects of China’s and U.S. GPR on industry stock market volatility are heterogeneous. Second, for most industries, China’s and U.S. GPR/IS can exacerbate industry stock market volatility both in bullish and bearish markets. In addition, China’s and U.S.GPR/IS and their interaction effects are heterogeneous and asymmetric, and the effects changes with the break point. Finally, compared with China’s GPR, the U.S. GPR has a larger impact on the industry stock market. The interactive effects of the U.S. GPR and IS can influence more industry stock market volatility.

This paper proposes the two-component realized EGARCH model with dynamic jump intensity (hereafter REGARCH-C-DJI model) to model and forecast stock market volatility. The key feature of our REGARCH-C-DJI model is its ability to exploit the high-frequency information as well as to capture the long memory volatility and jump dynamics. An empirical application to Shanghai Stock Exchange Composite (SSEC) index data shows the presence of high persistence of volatility and dynamic jumps in China’s stock market. More importantly, the REGARCH-C-DJI model dominates the GARCH, EGARCH, REGARCH and REGARCH-C models in terms of out-of-sample forecast performance. Our findings highlight the importance of accommodating the realized volatility, volatility components and jump dynamics in forecasting stock market volatility.

We investigate the cross-sectional factors of Chinese corporate bond returns via the reducedrank regression analysis (RRA) proposed by He et al. (2022). We collect 37 individual bond characteristics in the extant literature using a new dataset and construct 40 factor portfolios. Empirically, we find that the four-factor models created by RRA outperform the traditional factor models, PCA, and PLS factor models, both in-sample and out-of-sample. Among the 40 factors, the bond market factor is the most substantial predictor of future bond returns. In contrast, other factors provide limited incremental information for the cross-sectional pricing. Therefore, it is necessary to find more new bond factors. We further find that stock market anomalies do not improve the explanatory power of the RRA factor models. In particular, stock market anomalies can only partially explain the systematic part of bond returns in the RRA framework and have almost no explanatory power for the idiosyncratic component.

We investigate the relationship between ESG rating divergence and stock returns from an investor’s perspective, to explore the impact of inconsistency among ESG rating agencies on the capital market. We construct ESG rating divergence data using ratings from three prominent ESG rating agencies in China. Our study is based on 54,679 company-quarter observations from 2018 to 2022, which covers 4,377 Chinese listed companies. Our findings demonstrate a significant negative impact of ESG rating divergence on stock returns, which we validate through a series of robustness tests and endogenous analyses. Notably, we find that investors’ expectations mediate the relationship between ESG rating divergence and stock returns. Further analyses show that only the divergence in social ratings have a significant inhibitory effect on stock returns. In addition, ESG rating divergence significantly impedes subsequent average ESG ratings. The adverse relationship between ESG rating divergence and stock returns is particularly pronounced in non-heavy pollution companies, non-state-owned companies, and companies with lower external attention.

This paper explores the relationship between ESG rating disagreement and the stock price crash risk. Using 2011-2020 Chinese A-share listed companies in Shanghai and Shenzhen as research sample, the empirical test results show that ESG rating disagreement significantly increases the stock price crash risk. The mechanism tests find that ESG rating disagreement influences the stock price crash risk by undermining corporate information transparency and increasing the level of investor sentiment. The findings of this paper reveal the potential negative economic consequences of ESG rating disagreement and enrich the research on the influencing factors of stock price crash risk, which contribute to the prevention of possible financial risk and the sustainable development.

Using the firm-level data of the Chinese industrial sector from 1998 to 2010, this study investigates the impact of the previous environmental protection experience of prefecture-level Communist Party secretaries on the COD regulation within the secretaries’ respective jurisdictions. The study finds that the secretaries’ previous environmental protection experience has reduced the COD discharge intensity. The duration of the previous environmental protection experience is selected as an instrumental variable and the endogeneity is further addressed; the research conclusion remains unchanged. However, this negative impact only lasts for two years and presents an unclear long-term impact. The negative effect on COD discharge intensity caused by the previous environmental protection experience is affected by the mandatory regulation pressure from the central government and the overall polluting density of the sub-sectors. Secretaries with previous environmental protection experience do not reduce the COD discharge intensity by using the punishment mechanism of increasing sewage charges. The secretaries, instead, encourage enterprises to use clean production technology, save water resources, and reduce the produced COD level. Also, the secretaries place an emphasis on the treatment of wastewater pollutants, thus reducing the COD discharge intensity. The conclusions of this study can provide decisionmaking reference for the selection and training of local officials, with the goal of environmental regulation.

We investigate the dynamic extremal connectedness of sectors within the Chinese stock market conditional on exogenous extreme risk through multivariate extreme value regression. To proxy the exogenous extreme risk, we independently consider market volatility-based measures and policy uncertainty-based measures. We discover that market volatility-based measures have a stronger influence than policy uncertainty-based measures on the extremal connectedness of sectors. The oil volatility index is the most influential on extremal connectedness, and the energy sector plays a direct role in transmitting exogenous extreme risk. Our findings provide new insights into understanding the drivers of systematic and idiosyncratic contagion.