Purpose: Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.Design/methodology/approach: This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.Findings: The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.Originality/value: This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

Using the multifractal detrended cross-correlation analysis (MF-DCCA) method and the Empirical Mode Decomposition (EMD)-MF-DCCA method, this study quantifies the dynamic interrelation between carbon emission allowance returns in the Chinese and EU markets. The cross-correlation statistics indicate a moderate acceptance of the cross-correlation between the two carbon markets. Applying the MF-DCCA and EMD-MF-DCCA methods to the two markets reveals that their cross-correlation exhibits a power-law nature. Moreover, the apparent persistence of the cross-correlation and notable Hurst index show that the cross-correlation between long-term trends of the returns of the Guangdong and EU carbon emission markets exhibits stronger fractality over the long term, whereas the cross-correlation between the short-term fluctuations of the Hubei and EU carbon emission markets demonstrates stronger fractality. Subsequent investigations show that both fat tails and long memory contribute to the various fractals of the cross-correlation between the returns of the Chinese and EU carbon emission markets, especially for the fractals between the Hubei and EU carbon emission markets. Ultimately, the sliding window analysis demonstrates that national policy, trading activity, and other factors can make the observed multiple fractals more sensitive. The aforementioned findings facilitate an understanding of the current state of the Chinese carbon emission market and inform strategies for its future development.

Using international and Chinese prices of oil, coal, and natural gas, together with the Producer Price Index (PPI) of 40 industrial sectors in China from 2014 to 2026, this study constructs a tail-risk network including fossil energy markets and China’s industrial system using the TENET model. Then we analyze the transmission and dynamic evolution of extreme price risks from fossil energy markets to different Chinese industries. Empirical analysis reveals that the tail-risk spillovers from fossil energy markets to China’s PPI are significantly asymmetric. Specifically, (1) the intensity of tail-risk spillovers from energy markets to China’s industrial system is significantly greater than that in the opposite direction. Moreover, downside-risk spillovers from energy markets to the industrial system are stronger than upside-risk spillovers. (2) Natural gas is the most key node through which fossil energy price shocks are transmitted to China’s industrial system. (3) Within China’s industrial system, a few key sectors, such as Petroleum and Nuclear Fuel and Ferrous Metal Smelting & Rolling, play important roles in transmitting fossil energy price shocks throughout the Chinese industrial system. (4) Following the COVID-19 pandemic and the Russia–Ukraine conflict, spillovers from fossil energy markets to China’s PPI increased, although the core structure of risk transmission remained largely unchanged. In the downside-risk network, only the centrality of Coal Mining and Washing increased markedly after the COVID-19 pandemic, whereas Ferrous Metal Mining became more central following the Russia–Ukraine conflict.