Return and volatility network asymmetries between clean energy and fourth industrial revolution markets

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BENLİ M., ALTINTAŞ H.

SN Business and Economics, cilt.6, sa.10, 2026 (Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 6 Sayı: 10
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s43546-026-01461-6
  • Dergi Adı: SN Business and Economics
  • Derginin Tarandığı İndeksler: Scopus, ABI/INFORM
  • Anahtar Kelimeler: Clean energy, Fourth industrial revolution, GFEVD, Network topology, Quantile connectedness, Volatility
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Erciyes Üniversitesi Adresli: Evet

Özet

This study examines whether return and volatility shocks generate the same connectedness structure across clean energy and Fourth Industrial Revolution (4IR) markets. Using daily data from 2015 to 2026 for ten investable market proxies spanning clean energy, advanced computing, artificial intelligence and robotics, 3D printing, and biotechnology, we combine Quantile Vector Autoregression with generalized forecast error variance decomposition in a rolling-window framework. Dependence-aware bootstrap inference and network-topology measures complement the aggregate connectedness indicators. The results reveal pronounced state dependence. Return connectedness becomes system-wide in both tails, whereas volatility connectedness is more asymmetric, remaining comparatively segmented in the lower tail and becoming strongly synchronized in the upper tail. Even when aggregate return and volatility connectedness converge, their underlying network structures remain distinct, with volatility transmission more concentrated and the strongest bilateral links not fully overlapping with those in the return system. Clean-energy instruments emerge as robust net transmitters, whereas the 3D-printing segment contains the clearest net receiver under upper-tail volatility. The findings show that return and volatility spillovers should not be treated as interchangeable measures of market integration because common repricing and common uncertainty can generate different transmission architectures and timing.