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Journals / Uluslararası İşletme ve Ekonomi Çalışmaları Dergisi / 2026 / Cilt: 8 Sayı: 2

Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness

Asuman Erben Yavuz(BAŞKENT ÜNİVERSİTESİ)0000-0002-1934-5055
Pages
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Publication Date
2026-10-30
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

The findings reveal that the effects of geopolitical risk on AI and clean energy markets are nonlinear, heterogeneous, and dependent on market regimes and time horizons.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines the dynamic relationships among geopolitical risk, artificial intelligence (AI), and clean energy markets.
📊
Key Findings & Data:Using daily data from December 18, 2017 to May 1, 2026, the study employs Wavelet Quantile-on-Quantile Regression (WQQR), Quantile-on-Quantile Connectedness (QQCON), and Frequency Quantile Connectedness (FQCON) approaches.
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Conclusion & Impact:The findings reveal that the effects of geopolitical risk on AI and clean energy markets are nonlinear, heterogeneous, and dependent on market regimes and time horizons.
🔬Design:Araştırma Makalesi
📍Scope:2017 Verileri
⚙️Variables:Geopolitical risk · Artificial intelligence · Clean energy

Abstract

This study examines the dynamic relationships among geopolitical risk, artificial intelligence (AI), and clean energy markets. The Geopolitical Risk Index (GPR) is used to represent geopolitical risk; the STOXX Global Artificial Intelligence Index, Indxx Global Robotics & Artificial Intelligence Index, and Nasdaq CTA Artificial Intelligence & Robotics Index represent AI markets; and the FTSE Environmental Opportunities Renewable & Alternative Energy Index, NASDAQ Clean Edge Green Energy Index, and S&P Global Clean Energy Index represent clean energy markets. Using daily data from December 18, 2017 to May 1, 2026, the study employs Wavelet Quantile-on-Quantile Regression (WQQR), Quantile-on-Quantile Connectedness (QQCON), and Frequency Quantile Connectedness (FQCON) approaches. The findings reveal that the effects of geopolitical risk on AI and clean energy markets are nonlinear, heterogeneous, and dependent on market regimes and time horizons. Moreover, AI and clean energy markets exhibit asymmetric connectedness, while their net transmitter and receiver roles vary across quantiles and over time. These findings provide important implications for investors, portfolio managers, and policymakers regarding risk management, portfolio diversification, and the coordination of green and digital transformation policies.

Özet

This study examines the dynamic relationships among geopolitical risk, artificial intelligence (AI), and clean energy markets. The Geopolitical Risk Index (GPR) is used to represent geopolitical risk; the STOXX Global Artificial Intelligence Index, Indxx Global Robotics & Artificial Intelligence Index, and Nasdaq CTA Artificial Intelligence & Robotics Index represent AI markets; and the FTSE Environmental Opportunities Renewable & Alternative Energy Index, NASDAQ Clean Edge Green Energy Index, and S&P Global Clean Energy Index represent clean energy markets. Using daily data from December 18, 2017 to May 1, 2026, the study employs Wavelet Quantile-on-Quantile Regression (WQQR), Quantile-on-Quantile Connectedness (QQCON), and Frequency Quantile Connectedness (FQCON) approaches. The findings reveal that the effects of geopolitical risk on AI and clean energy markets are nonlinear, heterogeneous, and dependent on market regimes and time horizons. Moreover, AI and clean energy markets exhibit asymmetric connectedness, while their net transmitter and receiver roles vary across quantiles and over time. These findings provide important implications for investors, portfolio managers, and policymakers regarding risk management, portfolio diversification, and the coordination of green and digital transformation policies.

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Cite This Article

Yavuz, A. E. (2026). Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness. Uluslararası İşletme ve Ekonomi Çalışmaları Dergisi, Cilt: 8 Sayı: 2. https://doi.org/10.54821/bes.2029368

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