Time-varying asymmetric volatility spillovers among China's carbon markets, new energy market and stock market under the shocks of major events

被引:21
作者
Wu, Xinyu [1 ]
Jiang, Zhengting [1 ]
机构
[1] Anhui Univ Finance & Econ, Sch Finance, Bengbu 233030, Peoples R China
基金
中国国家自然科学基金;
关键词
Volatility spillovers; Asymmetric volatility; Major events; China's carbon markets; New energy market; Stock market; IMPULSE-RESPONSE ANALYSIS; OIL MARKET; CONNECTEDNESS; DEPENDENCE; PRICES; RETURN; BAD;
D O I
10.1016/j.eneco.2023.107004
中图分类号
F [经济];
学科分类号
02 ;
摘要
This paper investigates the dynamic asymmetric (good and bad) volatility spillover effects among China's carbon markets, new energy market and stock market by considering the volatility asymmetry in the markets. We infer the good and bad volatilities from the GJR-GARCH model, where they correspond to positive and negative shocks, respectively. Moreover, we extend the traditional (symmetric) marginal net spillover measure to asymmetric (good and bad) marginal net spillover measures to analyze the time-varying asymmetric transmission of volatility spillovers across markets based on external shocks of major events, including the Sino-US trade war, COVID-19 pandemic, and Russia-Ukraine conflict. Our empirical results show that there exists significantly time-varying asymmetric volatility spillover effects among China's carbon markets, new energy market and stock market. Moreover, the bad volatility spillover effect dominates the good volatility spillover effect. The asymmetric (good and bad) volatility spillovers across markets increase under the shocks of major events. In particular, we observe that the good volatility spillovers increase more significantly compared with the bad volatility spillovers during the Sino-US trade war and COVID-19 pandemic, while the bad volatility spillovers increase more significantly compared with the good volatility spillovers during the Russia-Ukraine conflict.
引用
收藏
页数:17
相关论文
共 47 条
[41]   High-dimensional nonlinear dependence and risk spillovers analysis between China's carbon market and its major influence factors [J].
Zhang, Shaobin ;
Ji, Hao ;
Tian, Maoxi ;
Wang, Binyao .
ANNALS OF OPERATIONS RESEARCH, 2025, 345 (2-3) :831-860
[42]   The dynamic volatility spillover between European carbon trading market and fossil energy market [J].
Zhang, Yue-Jun ;
Sun, Ya-Fang .
JOURNAL OF CLEANER PRODUCTION, 2016, 112 :2654-2663
[43]   Risk-return relationship and structural breaks: Evidence from China carbon market [J].
Zhao, Lili ;
Wen, Fenghua .
INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 2022, 77 :481-492
[44]   Do rare earths drive volatility spillover in crude oil, renewable energy, and high-technology markets? d A wavelet-based BEKK- GARCH-X approach [J].
Zheng, Biao ;
Zhang, Yuquan W. ;
Qu, Fang ;
Geng, Yong ;
Yu, Haishan .
ENERGY, 2022, 251
[45]   Carbon finance and carbon market in China: Progress and challenges [J].
Zhou, Kaile ;
Li, Yiwen .
JOURNAL OF CLEANER PRODUCTION, 2019, 214 :536-549
[46]   Identifying Strategic Traders in China's Pilot Carbon Emissions Trading Scheme [J].
Zhu, Lei ;
Wang, Xu ;
Zhang, Dayong .
ENERGY JOURNAL, 2020, 41 (02) :123-142
[47]   Global Systemic Financial Risk Spillovers and Their External Shocks [J].
Zihui Yang ;
Yinggang Zhou .
SOCIAL SCIENCES IN CHINA, 2020, 41 (02) :26-49