The impact of the Russia-Ukraine conflict on the energy subsector stocks in China: A network-based approach

被引:21
作者
Xing, Xiaoyun [1 ]
Xu, Zihan [1 ]
Chen, Ying [1 ]
Ouyang, WenPei [1 ]
Deng, Jing [1 ]
Pan, Huanxue [1 ]
机构
[1] Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China
关键词
The Russia-Ukraine conflict; Energy stock market; Risk transmission; Network analysis;
D O I
10.1016/j.frl.2023.103645
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
This paper explores the impact of the Russia-Ukraine conflict on the risk transmission of energy subsector stocks in China. Risk spillovers are quantified by the Diebold and Yilmaz index model. Based on the minimum spanning tree analysis, we identify the systematically important energy stocks and the shortest transmission paths. The results show that the key energy stocks in terms of volatility correlations would certainly change during the Russia-Ukraine conflict. In normal times, the traditional stocks act as the hubs of risk contagion. However, during the conflict, several renewable energy stocks tend to influence other stocks in the system.
引用
收藏
页数:8
相关论文
共 21 条
[1]  
[Anonymous], INT J FORECASTING, V28, P57
[2]   Multilayer financial networks and systemic importance: Evidence from China [J].
Cao, Jie ;
Wen, Fenghua ;
Stanley, H. Eugene ;
Wang, Xiong .
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 2021, 78
[3]   The US-China trade conflict impacts on the Chinese and US stock markets: A network-based approach [J].
Chen, Yanhua ;
Pantelous, Athanasios A. .
FINANCE RESEARCH LETTERS, 2022, 46
[4]   GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS [J].
Creal, Drew ;
Koopman, Siem Jan ;
Lucas, Andre .
JOURNAL OF APPLIED ECONOMETRICS, 2013, 28 (05) :777-795
[5]   Dynamic spillover effects and portfolio strategies between crude oil, gold and Chinese stock markets related to new energy vehicle [J].
Dai, Zhifeng ;
Zhu, Haoyang ;
Zhang, Xinhua .
ENERGY ECONOMICS, 2022, 109
[6]  
Deng M., 2022, Stock Prices and the Russia-Ukraine War: Sanctions, Energy and ESG
[7]   Better to give than to receive: Predictive directional measurement of volatility spillovers [J].
Diebold, Francis X. ;
Yilmaz, Kamil .
INTERNATIONAL JOURNAL OF FORECASTING, 2012, 28 (01) :57-66
[8]   Higher-order comoment contagion among G20 equity markets during the COVID-19 pandemic* [J].
Fry-McKibbin, Renee ;
Greenwood-Nimmo, Matthew ;
Hsiao, Cody Yu-Ling ;
Qi, Lin .
FINANCE RESEARCH LETTERS, 2022, 45
[9]   Modeling return and volatility spillover networks of global new energy companies [J].
Geng, Jiang-Bo ;
Du, Ya-Juan ;
Ji, Qiang ;
Zhang, Dayong .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2021, 135
[10]   Debt and financial market contagion [J].
Hsiao, Cody Yu-Ling ;
Morley, James .
EMPIRICAL ECONOMICS, 2022, 62 (04) :1599-1648