Asymmetric spillover connectedness between clean energy markets and industrial stock markets: How uncertainties affect it.
As the global climate crisis intensifies, clean energy is becoming increasingly important, and the intrinsic link between industry and energy highlights the connectedness between the industrial stock market and the clean energy market, and examining this connectedness can reveal risk spillovers betw...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316171 |
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| Summary: | As the global climate crisis intensifies, clean energy is becoming increasingly important, and the intrinsic link between industry and energy highlights the connectedness between the industrial stock market and the clean energy market, and examining this connectedness can reveal risk spillovers between these markets. We categorise the clean energy market into hydro, wind and solar markets, and the industrial stock market into low-carbon portfolios, high-carbon portfolios and ordinary portfolios, and use the network connectedness methodology to investigate the connectedness of returns between the clean energy submarkets and the industrial stock submarkets in the time and frequency domains. The returns are categorised into positive and negative returns in order to investigate the asymmetry in the connectedness of the markets. Finally, we explore the effects of EPU, GPU, and CPU in terms of network connectedness. It is revealed that clean energy submarkets are net receivers of risk, industrial stock submarkets are risk transmitters. The hydropower market is the main risk receiver, while the low-carbon portfolio is the main risk transmitter. Risk spillovers are mainly driven by short-term spillovers and do not have persistent spillover transmission. Bad news has a greater impact on network connectedness, leading to higher levels of connectedness between markets. EPU and CPU have significant effects on network connectedness. Our findings are informative for both investors and policymakers. |
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| ISSN: | 1932-6203 |