Network evolution underneath the volatility spillover in traditional and clean energy markets

ABSTRACT

When facing volatility spillovers in energy markets, all players require risk mitigation strategies to insulate themselves from the same. To prevent energy markets from being strongly crashed by volatility spillovers, which even trigger financial crises, in this paper, we use network analysis as an aid to identify spillovers among the main nine energy markets. Specifically, we first measure the volatility spillovers among the main energy markets through a BEKK model. Based on this, influential markets are identified by using network analysis. The coal, wind and water energy markets should be paid close attention as they occupy vital roles in the volatility spillover network. Even though clean energy markets contribute more in terms of market stability, traditional energy markets are still important to ensure energy supply when experiencing extreme crashes caused by COVID-19. In this paper, we make the contributions to analysing volatility spillovers in multiple energy markets and identifying crucial energy markets in volatility spillover networks, then provide more market information that helps the government and policymakers effectively manage systemic risks caused by volatility spillovers. The effective risk management of crucial energy markets enhances economic recovery and stability, especially in the post-COVID-19 era.

KEYWORDS:

• Energy market
• volatility spillover
• crucial market identification
• cliques analysis
• network evolution

JEL CLASSIFICATION:

• Economic Development-Agriculture
• Natural Resources
• Energy
• Environment
• Other Primary Products

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/00036846.2023.2166663

Notes

¹ Based on the sample size of 2912, it can be found that gas obtains the highest average returns, while coal has the lowest. Compared with the conventional energy market, the average returns in the clean energy market are more stable. A similar phenomenon could also be found on the aspect of standard deviations. The JB statistics are consistently rejected at 1% significance level. The ADF test suggests that almost all the range time series are stationary at the 1% significance. All sample stock indices’ returns are negatively skewed, except for fuel cell, geothermal, oil and gas energy markets. Besides, the kurtosis statistics values are more than 3, indicating that the distributions are non-normal with fatter tails and high peaks.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [72071176 and 71840001]; Yunnan Province of Philosophy and Social Science Program [QN202211]; The Philosophy and Social Science Innovation Team Project of Yunnan Province [No. 2022CX01].