The dynamic risk spillover of higher-order moments in the China’s energy market: A time-frequency perspective DOI
Xueyong Liu, Binbin Wang,

Min Luo

et al.

International Journal of Green Energy, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 19

Published: Dec. 29, 2024

This study employs a risk spillover measurement method based on CEEMDAN-SE, GARCHSK, and TVP-VAR-DY models to assess among 11 sub-sectors in China's energy market, such as coal, oil, thermal power, new vehicles. The sector index return is decomposed into intrinsic mode functions (IMFs) reconstructed high, medium, low-frequency sequences. GARCHSK model calculates conditional mean, variances, skewness, kurtosis sequences for these sequences, which are then integrated the evaluate return, volatility, higher-order moment spillovers. Empirical findings highlight significant time-varying effects, with volatility spillovers surpassing average total skewness New Energy Vehicle (NEV) Electric Power Grid (EPG) sectors act major transmitters, while primary receivers vary by frequency order. Regulatory authorities should develop real-time surveillance mechanism monitor transfer of risks between NEV EPG sector. It essential foster inter-industry cooperation facilitate better resource allocation swift response emerging challenges. Furthermore, policymakers focus bolstering resilience pivotal employing dynamic management strategies providing appropriate incentives.

Language: Английский

Measuring multi-scale risk contagion between crude oil, clean energy, and stock market: A MODWT-Vine-copula method DOI
Yaling Chen, Huiming Zhu, Yinpeng Liu

et al.

Research in International Business and Finance, Journal Year: 2025, Volume and Issue: unknown, P. 102790 - 102790

Published: Jan. 1, 2025

Language: Английский

Citations

1
Extreme risk spillovers between SC, WTI and Brent crude oil futures-Evidence from Time-varying Granger causality test DOI
Xiaohang Ren, Yue He,

Chuanwang Liu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135495 - 135495

Published: March 1, 2025

Language: Английский

Citations

1
Greening the energy industry: An efficiency analysis of China's listed new energy companies and its market spillovers DOI
Xiaohang Ren, Shen Wang, Weifang Mao

et al.

Energy Economics, Journal Year: 2025, Volume and Issue: unknown, P. 108414 - 108414

Published: March 1, 2025

Language: Английский

Citations

1
Geopolitical risk and vulnerability of energy markets DOI
Zhenhua Liu, Yushu Wang, X. Yuan

et al.

Energy Economics, Journal Year: 2024, Volume and Issue: unknown, P. 108055 - 108055

Published: Nov. 1, 2024

Language: Английский

Citations

4
Can Chinese investors manage climate risk domestically and globally? DOI
Yike Liu,

Zihan Xu,

Xiaoyun Xing

et al.

International Review of Economics & Finance, Journal Year: 2024, Volume and Issue: unknown, P. 103664 - 103664

Published: Oct. 1, 2024

Language: Английский

Citations

1
Long-span multi-layer spillovers between moments of advanced equity markets: The role of climate risks DOI
Matteo Foglia, Vasilios Plakandaras,

Rangan Gupta

et al.

Research in International Business and Finance, Journal Year: 2024, Volume and Issue: unknown, P. 102667 - 102667

Published: Nov. 1, 2024

Language: Английский

Citations

1
Complexity and synchronization of carbon and new energy markets based on multiscale entropy DOI Creative Commons
Jianru Fu, Ying Sun,

Xiaonan Liu

et al.

Energy Science & Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 29, 2024

Abstract This study provides a comprehensive analysis of the dynamic evolution complexity and synchrony between carbon market new energy from 2019 to 2023, employing multiscale sample entropy cross‐sample methods. The shows that, both in long term short term, consistently exceeds that market. Additionally, markets exhibit higher synchronization on smaller scales. As time scale increases, interactions become more complex diverse. According these findings, it is recommended policymakers improve transparency market, optimize operational mechanisms ensure reduction strategies policies are mutually coordinated. holds significant importance for targets development energy.

Language: Английский

Citations

0
The dynamic risk spillover of higher-order moments in the China’s energy market: A time-frequency perspective DOI
Xueyong Liu, Binbin Wang,

Min Luo

et al.

International Journal of Green Energy, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 19

Published: Dec. 29, 2024

This study employs a risk spillover measurement method based on CEEMDAN-SE, GARCHSK, and TVP-VAR-DY models to assess among 11 sub-sectors in China's energy market, such as coal, oil, thermal power, new vehicles. The sector index return is decomposed into intrinsic mode functions (IMFs) reconstructed high, medium, low-frequency sequences. GARCHSK model calculates conditional mean, variances, skewness, kurtosis sequences for these sequences, which are then integrated the evaluate return, volatility, higher-order moment spillovers. Empirical findings highlight significant time-varying effects, with volatility spillovers surpassing average total skewness New Energy Vehicle (NEV) Electric Power Grid (EPG) sectors act major transmitters, while primary receivers vary by frequency order. Regulatory authorities should develop real-time surveillance mechanism monitor transfer of risks between NEV EPG sector. It essential foster inter-industry cooperation facilitate better resource allocation swift response emerging challenges. Furthermore, policymakers focus bolstering resilience pivotal employing dynamic management strategies providing appropriate incentives.

Language: Английский

Citations

0