Climate Change Concerns and Financial Market Returns: Distributional Predictability and Local State Dependence Across Technology, Clean Energy and Carbon-Intensive Assets

Altıntaş H., Benli M.

INTERNATIONAL SOCIAL SCIENCE JOURNAL, ss.1-20, 2026 (Scopus)

Özet

This study examines how climate change concerns are associated with technology-oriented, clean-energy and carbon-intensive energy returns using daily data from 23 April 2018 to 30 June 2025. The empirical framework combines quantile Granger noncausality testing, quantile-on-quantile regression (QQR), pointwise moving-block-bootstrap inference and an aggregation-based QR–QQR benchmark. The Granger results reveal limited Overall predictability, with only the CCS-to-SOG direction significant in the omnibus test, whereas technology-oriented and clean-energy assets display localized predictive content at selected quantiles, mainly in upper-return states. QQR estimates show substantial local heterogeneity, but bootstrap confidence bounds indicate that statistically supported associations are concentrated in a limited number of asset-specific quantile combinations, with ICLN showing the strongest localized support. The QR–QQR benchmark furtherreveals particularly close broad-profile correspondence for carbon-intensive energy assets. Overall, the financial relevance of climate change concerns is better characterized as selective, localized and asset dependent than as pervasive across markets or distributional states.