This paper develops a network to analyze inter-industry risk spillovers during high and low volatility periods. Our findings indicate that China's Industrials and Consumer Discretionary exhibit the greatest levels of spillovers in both high and low volatility states. Notably, our results demonstrate the "event-driven" character of structural changes to the network during periods of pronounced risk events. At the same time, the economic and financial network exhibits clear "small world" characteristics. Additionally, in the high volatility stage, the inter-industry risk contagion network becomes more complex, featuring greater connectivity and direct contagion paths. Furthermore, concerning the spillover connection between finance and the real sector, the real economy serves as a net exporter of risk. The study's findings can assist government agencies in preventing risk contagion between the financial market and the real economy. The empirical evidence and policy lessons provide valuable insights for effective risk management.

As the world’s two largest economies, China and the US are currently experiencing political and economic friction. This conflict brings high uncertainty to financial markets. Assessing risk spillover effects in a sector level will help us to characterize international risk contagions. We construct a multilayer network to examine tail risk spillovers between China and the US and find that (1) the value of total connectedness rises amidst tensions but declines during reconciliations; (2) interlayer spillovers mainly manifest as extreme pulses instead of steady outflows, which implies a significant increase in the frequency and magnitude of interlayer spillovers requires vigilant monitoring; and (3) compared with the in-strength, the out-strength is more concentrated, which represents that some sectors may play the role of major interlayer transmitter in tail risk spillovers. Monitoring interlayer spillovers helps policymakers and investors respond to emerging systemic threats.

Climate change shocks pose a threat to the stability of the financial system. This study examines the influence of climate risks on systemic risk in the Chinese market by utilizing extreme risk spillover network. Moreover, we construct climate risk indices for physical risks (abnormal temperature), and transition risks (Climate Policy Uncertainty). We demonstrate a significant increase in systemic risk due to climate risks, which can be attributed, in part, to investor sentiment. Furthermore, institutional investors can mitigate the adverse impact of climate risks. Our findings suggest that policymakers and investors need to exercise greater vigilance in addressing climaterelated adverse effects.

The asymmetric dissemination of information among financial firms in the financial market reflects their asymmetric response to the dissemination of both positive and negative information. However, it is worth studying whether this asymmetry will intensify or alleviate under different financial market conditions. Based on high-frequency minute stock price data of Chinese A-share listed financial firms from July 2020 to July 2023, we decompose the good and bad information, as well as the positive and negative volatility information in the return series. We utilize the quantile cross-spectral correlation method to construct an information overflow network at monthly intervals. We use the MVMQ-CAViaR model to estimate the value at risk (VaR) for various quantiles and build a risk spillover network that incorporates both positive and negative tail risk information, using the quantile dynamic SIM-COVAR-TENET model. We calculated the network dissemination efficiency of both good and bad information, including average speed, speed deviation, densest speed, and depth, to explore the changes in the asymmetry of good and bad information dissemination under different financial market conditions. We get that when the financial market is booming, financial firms’ asymmetric response to good and bad information will increase, and the firms will pay more attention to bad information. When the financial market declines, the asymmetric response of financial firms to good and bad information is diminished, and their sensitivity to both positive and negative information is heightened. In addition, the dissemination of bad information by firms in the five sub-financial industries across various markets exacerbates the asymmetric response of other financial firms to good and bad information. More importantly, the release of positive return information, negative volatility information, and highly negative tail risk information by the real estate financial firms all impact the asymmetric response of financial firms to good and bad information in a prosperous financial market. In recessionary financial markets, financial regulators can strategically release positive information to mitigate the decline in the financial market. Conversely, in a booming financial market, financial regulators should be cautious of the negative impact that bad information can have on financial firms, particularly in relation to the excessive growth of the real estate sector and the potential chain reaction of significant adverse events.

This paper examines the different responses of local and foreign investors to host government risk perceptions in the context of extreme events. We develop COVID-19 attention indices that capture attention related to COVID-19 according to China Central Television (CCTV) news program and further construct the government’s risk perception (GRPC) measure about COVID-19. Given the cross-listed AH-shares in China, we find that GRPC caused the extreme movement of stock markets by applying the multi-quantile VaR Granger causality approach. The results show that the reaction of cross-listed stocks in the A-share market is more inflexible than that in the H-share market during the outbreak period of the pandemic, foreign investors follow GRPC as a weather vane than local investors, and both types of investors are more concerned about the pessimism of GRPC. In the period of epidemic normalization, local and foreign investors prefer the optimistic attitude conveyed by the Chinese government.

The asymmetric dissemination of information among financial firms in the financial market reflects their asymmetric response to the dissemination of both positive and negative information. However, it is worth studying whether this asymmetry will intensify or alleviate under different financial market conditions. Based on high-frequency minute stock price data of Chinese A-share listed financial firms from July 2020 to July 2023, we decompose the good and bad information, as well as the positive and negative volatility information in the return series. We utilize the quantile cross-spectral correlation method to construct an information overflow network at monthly intervals. We use the MVMQ-CAViaR model to estimate the value at risk (VaR) for various quantiles and build a risk spillover network that incorporates both positive and negative tail risk information, using the quantile dynamic SIM-COVAR-TENET model. We calculated the network dissemination efficiency of both good and bad information, including average speed, speed deviation, densest speed, and depth, to explore the changes in the asymmetry of good and bad information dissemination under different financial market conditions. We get that when the financial market is booming, financial firms’ asymmetric response to good and bad information will increase, and the firms will pay more attention to bad information. When the financial market declines, the asymmetric response of financial firms to good and bad information is diminished, and their sensitivity to both positive and negative information is heightened. In addition, the dissemination of bad information by firms in the five sub-financial industries across various markets exacerbates the asymmetric response of other financial firms to good and bad information. More importantly, the release of positive return information, negative volatility information, and highly negative tail risk information by the real estate financial firms all impact the asymmetric response of financial firms to good and bad information in a prosperous financial market. In recessionary financial markets, financial regulators can strategically release positive information to mitigate the decline in the financial market. Conversely, in a booming financial market, financial regulators should be cautious of the negative impact that bad information can have on financial firms, particularly in relation to the excessive growth of the real estate sector and the potential chain reaction of significant adverse events.

Based on the connectedness time-frequency domain decomposition method are adopted in this paper. With the help of network topology for visualization, the characteristics and transmission path of financial risks in the time-frequency domain under major emergencies are studied. The results show that after the occurrence of MPEs, the level of risk spillover in China's financial market usually decreases in the short term, medium term and long term. When the policy has a long time lag or the market reaction is not timely, the medium term risk spillover will be higher than the short term risk spillover.

Starting from document research, this paper analyzes the mechanism of the risk spillover effect from developed capital markets to the Chinese capital market. After that, this paper conducts an empirical study on the risk spillover effect of developed capital markets on the Chinese capital market by using the DCC-GARCH model. Then the impact degree of global major stock market fluctuations on the Chinese stock market is measured. The analysis shows that there exists a significant risk spillover effect of developed capital markets on the Chinese capital market, but the effect began to weaken after the financial crisis and the size of the spillover effect can be affected by macro factors such as geographical locations, foreign trade, and foreign investment.

The crypto and noncrypto markets used to be separated from each other. We argue that with the rapid development of stablecoins since 2018, risks are now transmitted between the crypto and noncrypto markets through stablecoins, which are both pegged to noncrypto assets and play a central role in crypto trading. Applying copula-based CoVaR approaches, we find significant risk spillovers between stablecoins and cryptocurrencies as well as between stablecoins and noncrypto markets, which could help explain the tail dependency between the crypto and noncrypto markets from 2019 to 2021. We also document that the risk spillovers through stablecoins are asymmetric—stronger in the direction from the US dollar to the crypto market than vice versa—which suggests the crypto market is re-dollarizing. Further analyses consider alternative explanations, such as the COVID-19 pandemic and institutional crypto holdings, and determine that the primary channels of risk transmission are stablecoins’ US dollar peg to the noncrypto market and their transaction-medium function in the crypto ecosystem. Our results have important implications for financial stability and shed light on the future of stablecoin regulation.

The crypto and noncrypto markets used to be separated from each other. We argue that with the rapid development of stablecoins since 2018, risks are now transmitted between the crypto and noncrypto markets through stablecoins, which are both pegged to noncrypto assets and play a central role in crypto trading. Applying copula-based CoVaR approaches, we find significant risk spillovers between stablecoins and cryptocurrencies as well as between stablecoins and noncrypto markets, which could help explain the tail dependency between the crypto and noncrypto markets from 2019 to 2021. We also document that the risk spillovers through stablecoins are asymmetric—stronger in the direction from the US dollar to the crypto market than vice versa—which suggests the crypto market is re-dollarizing. Further analyses consider alternative explanations, such as the COVID-19 pandemic and institutional crypto holdings, and determine that the primary channels of risk transmission are stablecoins' US dollar peg to the noncrypto market and their transaction-medium function in the crypto ecosystem. Our results have important implications for financial stability and shed light on the future of stablecoin regulation.