image: It can be seen that the global efficiency curves of the three layers are slightly different. More specifically, the RK layer shows the most obvious downward trend, while the RV layer has the slowest downward trend, and the RS layer falls between RK layer and RV layer. This phenomenon indicates that the interconnectedness between the variables based on the three indicators have different characteristics. Thus, it is necessary to construct a multilayer connectedness network by combining the RV, RS and RK to explore the interconnectedness between the financial sectors and the new energy companies of China. As shown by the dashed line in Figure 1, the key point of the global efficiency curves is around 0.1. In other words, when the threshold exceeds 0.1, the global efficiency of all layers will collapse. In view of this, 0.1 is the effective threshold selected in this paper.
Credit: Zhifeng Dai and Haoyang Zhu (Changsha University of Science and Technology, China).
Background and Motivation
As China accelerates its transition toward a low-carbon economy, the new energy sector has become a strategic pillar, drawing increasing attention and capital from the financial system. With this capital influx comes heightened concern about systemic risk spillovers between financial institutions and new energy firms, particularly during periods of market stress. Traditional single-layer models fail to capture the complex, multi-dimensional risk interactions across sectors.
China Finance Review International (CFRI) presents the article titled "Multilayer Network Analysis for the Interconnectedness Between Financial Sectors and New Energy Companies in China", which investigates these relationships through a multilayer network framework using high-frequency data.
Methodology and Scope
The authors construct a multilayer connectedness network based on three realised indicators—realised volatility (RV), realised skewness (RS), and realised kurtosis (RK)—all derived from 5-minute intraday trading data covering 2012–2022. They apply the Diebold-Yilmaz (DY) connectedness framework, enhanced with a LASSO-VAR model, to 25 companies: 13 listed banks, 3 insurance firms, and 9 leading new energy firms ranked by market capitalisation.
System- and company-level indicators are computed to evaluate not only overall connectedness but also the direction and strength of risk spillovers within and across layers.
Key Findings and Contributions
- Multidimensional Risk Capture: RV, RS, and RK layers provide complementary insights into risk contagion. Volatility captures persistent risk, while skewness and kurtosis reflect jump and tail risks, respectively.
- Banks as Risk Hubs: On average, banks act as net transmitters of systemic risk, while insurance and new energy companies are net receivers.
- Temporal Dynamics: Risk contagion is time-varying, intensifying notably during crises such as the 2015–16 Chinese stock market crash and the COVID-19 outbreak in 2020.
- Layer-Specific Roles: Some institutions switch roles (e.g., from risk sender to receiver) depending on the risk type captured in different layers.
- Structural Insight: The multilayer network shows that interconnectivity tends to cluster across layers, especially in turbulent times, making single-layer analysis insufficient for comprehensive risk assessment.
Why It Matters
The study offers a more granular and dynamic understanding of how systemic risk propagates between critical sectors in China. By integrating multiple layers of risk measurement, it provides a more robust framework for monitoring financial stability in an era of green transformation and increasing financialization of the energy market.
Practical Applications
- For Investors: The multilayer network helps investors better understand systemic risks associated with financial institutions and new energy firms. Risk transmission patterns can guide dynamic portfolio adjustment under different market conditions.
- For Managers (of financial and energy firms): Understanding time-varying interconnectedness can inform forward-looking strategies. Layer-specific risk exposure insights support better internal risk control mechanisms.
- For Policymakers: The dominant role of banks as systemic risk hubs underlines the need for stronger supervision and regulatory preparedness. The model provides actionable insights into how financial shocks can impact strategic sectors like new energy, helping inform targeted intervention strategies.
Discover high-quality academic insights in finance from this article published in China Finance Review International. Click the DOI below to read the full-text original! Open access for a limited time!
Journal
China Finance Review International
Method of Research
News article
Article Title
Multilayer network analysis for the interconnectedness between financial sectors and new energy companies in China
Article Publication Date
5-Jun-2025