Abstract

Purpose – Climate change has emerged as one of the new sources of financial risk, but it is still not recognized as a significant influencing factor in existing studies, especially in China. This study aims to investigate how climate policy changes in China affect intersectoral systemic risk from a mixed frequency model perspective.

Design/methodology/approach – We include asymmetric tail long memory for the dependence, which has not been covered by other risk-related literature, in the study of China’s sector risk contribution by proposing the TVM-MIDAS Copula model-based MES approach. Besides, we construct the GARCH-MIDAS-CPU model to investigate the impact of CPU on the contribution of systemic risk in the sector.

Findings – The results show that the real estate sector has the greatest tail dependence on the market, the raw materials sector has the longest memory of upper tail dependence, and the consumer sector has a weaker link to the market. For CPU, when the market falls moderately, CPU amplifies the volatility of the systematic risk contribution of the energy, materials, industrials, and real estate sectors and reduces the volatility of the risk contribution of the consumer, healthcare, and financial sectors. When the market plummets, the CPU amplifies the intensity of the volatility of systemic risk contributions from all sectors except the healthcare sector.

Originality/value – First, this paper analyzes how CPU influences systemic risk within Chinese sectors, offering confident evidence of the link between climate policy changes and sectoral risks. Second, it proposes a TVM-MIDAS copula model to capture dynamic tail dependence with tail memory advantages. Third, it utilizes a GARCH-MIDAS-CPU mixed-frequency model to examine the heterogeneous impact of CPU on systemic risk across sectors, addressing the co-frequency data downsampling issue and providing more precise insights.

Abstract

Purpose – We investigate latent higher-order dependencies in Chinese sectoral risk connectedness networks, characterize their topology and quantify resilience at both the system and sector levels, thereby offering new insights for mitigating systemic risk and preserving financial stability.

Design/methodology/approach – Employing the RHOSTS approach, we construct higher-order risk connectedness networks for Chinese stock sectors and analyze their structure with network-topology metrics. These metrics are then embedded in a coupled-map-lattice model to track the time-varying resilience of the overall network and its constituent sectors.

Findings – The sectoral network exhibits pronounced higher-order interactions, with four-sector synchronous resonance as the prevailing motif. Shock-specific core resonance clusters emerge and although system-wide resilience increases over time, marked heterogeneity across sectors persists.

Originality/value – By moving beyond traditional pairwise spillover models, our higher-order financial network reveals collective risk resonance spanning multiple sectors. The topology-based metrics we propose enable simultaneous assessment of system-level and sector-specific resilience and its evolution.