Artificial intelligence, clean, and dirty energy markets: a quantile-on-quantile connectedness analysis of volatility transmission
ECONOMIC CHANGE AND RESTRUCTURING, cilt.59, ss.1-59, 2026 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 59
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s10644-026-10037-1
- Dergi Adı: ECONOMIC CHANGE AND RESTRUCTURING
- Derginin Tarandığı İndeksler: Social Science Premium Collection (ProQuest), Business Source Ultimate (EBSCO), Health Research Premium Collection (ProQuest), Scopus, Pharma Collection (ProQuest), Social Sciences Citation Index (SSCI), IBZ Online, ABI/INFORM, EconLit, Geobase
- Sayfa Sayıları: ss.1-59
- Bilecik Şeyh Edebali Üniversitesi Adresli: Evet
Özet
The growing interaction between artificial intelligence (AI), clean energy, and dirty energy markets has created new channels of systemic risk that cannot be captured by linear or mean-based frameworks. This study examines the nonlinear and quantile-dependent volatility spillovers across these markets over the period 2018–2025 using a Quantile-on-Quantile Connectedness (QQC) approach, which allows spillovers to vary across market states and over time. The results reveal a strongly asymmetric and state-dependent transmission structure. AI-related assets are associated with the strongest net transmitting positions in volatility connectedness, particularly in upper-tail and high volatility regimes. Clean energy markets are more frequently observed in net receiving positions in the short run, but their connectedness profiles become more net transmitting over longer horizons as green-transition dynamics strengthen. Dirty energy assets are more often associated with net receiving and weaker outward spillover positions during turbulent periods while generating relatively weaker feedback effects under normal conditions. Dynamic evidence further shows that connectedness intensifies sharply during major global disruptions, especially the COVID-19 pandemic and the Russia-Ukraine conflict, confirming that tail-related spillover patterns are more pronounced within the connectedness structure. Overall, the findings show that volatility linkages between innovation and energy markets are nonlinear, time-varying, and highly regime-specific. These results provide important implications for portfolio diversification, hedging strategies, and policy coordination in an increasingly interconnected financial system.