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

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  • Wu, Xinyu
  • Jiang, Zhengting

Abstract

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.

Suggested Citation

  • Wu, Xinyu & Jiang, Zhengting, 2023. "Time-varying asymmetric volatility spillovers among China’s carbon markets, new energy market and stock market under the shocks of major events," Energy Economics, Elsevier, vol. 126(C).
  • Handle: RePEc:eee:eneeco:v:126:y:2023:i:c:s0140988323005029
    DOI: 10.1016/j.eneco.2023.107004
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    More about this item

    Keywords

    Volatility spillovers; Asymmetric volatility; Major events; China’s carbon markets; New energy market; Stock market;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G1 - Financial Economics - - General Financial Markets
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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