Research on tail risk contagion in international energy markets—The quantile time-frequency volatility spillover perspective

• Published in: Energy economics Vol. 121; p. 106678
• Main Authors: Gong, Xiao-Li, Zhao, Min, Wu, Zhuo-Cheng, Jia, Kai-Wen, Xiong, Xiong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2023
• Subjects: Clean energy; Conditional quantile; Frequency domain; Tail risk spillover; Frequency domain; Tail risk spillover; Conditional quantile; Clean energy
• ISSN: 0140-9883; 1873-6181
• DOI: 10.1016/j.eneco.2023.106678

From the perspective of volatility spillover network, this paper constructs the spillover index based on the quantile vector autoregression (QVAR) model to capture tail risk spillover effects in international energy markets under different shock sizes and frequency domains. Using the traditional and clean energy markets data covering 2010 to 2022, the empirical results show that the spillover index based on the QVAR model can better capture the tail risk spillover effects with different shock sizes, while the conditional mean-based spillover index may underestimate or misjudge the true level of tail risk spillover among markets. In the extreme state, the tail risk spillover effect among energy markets is significantly enhanced compared to the normal state. From the perspective of frequency domain, it is found that the total tail risk spillovers among international energy markets are dominated by long-term risk spillovers. At the same time, the asymmetry of tail risk spillover effect is more remarkable in the extreme state in the long-term. In addition, the traditional energy markets are the main sources of tail risk spillover, and mainly play the role of net risk spillover. In contrast, the fuel cell, geothermal energy and solar energy markets in the clean energy market have greater net spillover-in effect and play the net receiver role in the interconnectedness network. •Use ARMA-EGARCH-Skew-t thick-tailed distribution to characterize energy volatility.•Quantile time-frequency spillover framework is employed to measure energy tail risk spillover.•Time-frequency spillover networks are used to describe tail risk contagion among energy markets.•The energy tail risk network exhibits market heterogeneity and cyclical characteristics.•The total tail risk spillover among energy markets is dominated by long-term risk spillover.