Extreme frequency connectedness between clean energy, fossil fuel, and G7 stock markets: Portfolio management implications

This study examines the connectedness and risk spillovers between G7 stock markets, oil (Brent, WTI, Gasoline, Heating Oil), and clean energy (S&P GCE, Rennix, Wilderhill) markets. Using the Quantile Frequency Connectedness framework, we assess interconnectedness across different market phases and investment horizons. The results reveal that connectedness is time-varying and intensifies significantly during periods of financial turmoil, such as the COVID-19 epidemic. Short-term spillovers dominate, suggesting rapid dissemination of shocks. The connectedness structure is also asymmetric, with stronger spillovers in bearish phases. G7 stock indices emerge as consistent net transmitters of volatility, while clean energy assets function as net receivers during bearish phases, offering diversification benefits and serving as safe-haven assets. In contrast, oil assets shift from net receivers to net transmitters over longer horizons. Portfolio analysis reveals that although oil assets offer stronger hedging effectiveness, clean energy assets outperform in terms of risk-adjusted returns and serve as effective volatility buffers. Such benefits reinforce their role in sustainable and resilient portfolio construction. These findings carry significant implications for investors and policymakers, highlighting the critical role of asymmetric and frequency-dependent market connectedness in understanding risk transmission, and promoting the clean energy transition through the implementation of robust green financial policies.

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Publication Details

Journal
Innovation and Green Development
Published
2026-01-08
DOI
https://doi.org/10.1016/j.igd.2026.100324
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
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article

Extreme frequency connectedness between clean energy, fossil fuel, and G7 stock markets: Portfolio management implications

Khamis Hamed Al‐Yahyaee, Houssem Eddine Belghouthi, Walid Mensi
Innovation and Green Development
Market Dynamics and Volatility
article

Extreme frequency connectedness between clean energy, fossil fuel, and G7 stock markets: Portfolio management implications

Khamis Hamed Al‐Yahyaee, Houssem Eddine Belghouthi, Walid Mensi
article en

Abstract

This study examines the connectedness and risk spillovers between G7 stock markets, oil (Brent, WTI, Gasoline, Heating Oil), and clean energy (S&P GCE, Rennix, Wilderhill) markets. Using the Quantile Frequency Connectedness framework, we assess interconnectedness across different market phases and investment horizons. The results reveal that connectedness is time-varying and intensifies significantly during periods of financial turmoil, such as the COVID-19 epidemic. Short-term spillovers dominate, suggesting rapid dissemination of shocks. The connectedness structure is also asymmetric, with stronger spillovers in bearish phases. G7 stock indices emerge as consistent net transmitters of volatility, while clean energy assets function as net receivers during bearish phases, offering diversification benefits and serving as safe-haven assets. In contrast, oil assets shift from net receivers to net transmitters over longer horizons. Portfolio analysis reveals that although oil assets offer stronger hedging effectiveness, clean energy assets outperform in terms of risk-adjusted returns and serve as effective volatility buffers. Such benefits reinforce their role in sustainable and resilient portfolio construction. These findings carry significant implications for investors and policymakers, highlighting the critical role of asymmetric and frequency-dependent market connectedness in understanding risk transmission, and promoting the clean energy transition through the implementation of robust green financial policies.

Innovation and Green DevelopmentVol. 5(1)
Muscat College (OM), Sultan Qaboos University (OM), Tunis El Manar University (TN)
Industry, innovation and infrastructure
Openalex Percentile: Top 96%
Market Dynamics and Volatility
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