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Author:

Gao, Y. (Gao, Y..) | Zhou, Y. (Zhou, Y..) | Zhao, L. (Zhao, L..)

Indexed by:

SSCI Scopus

Abstract:

This study examines the risk spillover among green financial and energy markets, including the green bond, green stock, carbon, new and traditional energy markets, and utilizes the quantile-on-quantile regression and quantile connectedness methods to study the pairwise interaction and connectivity at different quantiles. We find significant differences and time-varying characteristics in markets’ volatility. The results of quantile-on-quantile regressions further reveal heterogeneity in pairwise market interaction under different volatility conditions. Considering the impact of China's economic policy uncertainty index, the quantile connectedness results indicate that risk spillovers strengthen in the high quantile and weaken in the low or middle quantile cases. Under most conditions, the green stock market serves as a transmitter, whereas the green bond market serves as a receiver. The analysis in subsamples further reveals that special events could increase risk spillovers, leading to severe fluctuations, especially for intermediate quantiles. These findings will provide a reference for investors to construct portfolios and policymakers to improve risk supervision. © 2024 Economic Society of Australia, Queensland

Keyword:

Quantile connectedness Economic policy uncertainty Green financial markets Quantile-on-quantile regression

Author Community:

  • [ 1 ] [Gao Y.]School of Economics and Management, Beijing University of Technology, Beijing, China
  • [ 2 ] [Zhou Y.]School of Economics and Management, Beijing University of Technology, Beijing, China
  • [ 3 ] [Zhao L.]School of Management, Northwestern Polytechnical University, Shaanxi, Xi'An, China

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Source :

Economic Analysis and Policy

ISSN: 0313-5926

Year: 2024

Volume: 81

Page: 1148-1177

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 7

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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