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Impact of Climate Transition and Physical Risks on Clean–Dirty Stock Connectedness across Market Conditions
Journal article   Open access   Peer reviewed

Impact of Climate Transition and Physical Risks on Clean–Dirty Stock Connectedness across Market Conditions

Hela BEN HAMIDA and Shawkat Hammoudeh
Borsa Istanbul Review, Forthcoming
May 2026
DOI: https://doi.org/10.1016/j.bir.2026.100838
Featured in Collection :   Drexel's Newest Publications
url
https://doi.org/10.1016/j.bir.2026.100838View
Published, Version of Record (VoR) Open

Abstract

This study examines the role of climate transition and climate physical risk in explaining the time-varying correlation between clean and dirty stocks under different market conditions. We employ three complementary and supportive frameworks: the multivariate Glosten, Jagannathan and Runkle GARCH (M-GJR-GARCH) model with dynamic conditional correlation (DCC), the autoregressive distributed lag (ARDL) model, and the wavelet quantile correlation (WQC) method, using a data sample spanning January 2015 to January 2025. We extend our standard GARCH-ARDL baseline analysis by integrating the WQC to examine the relationship among climate-aware, clean, and dirty stocks across different investment horizons and market states. The MJGR-GARCH-DCC model indicates that clean and dirty stocks are negatively and weakly correlated, with substantial time-varying patterns. The ARDL model indicates that, in the long run, only attention to climate physical risk adversely affects the clean–dirty correlation, while climate transition risk is insignificant. The WQC analysis demonstrates that the relationship between climate risk attention and stock correlation varies across time scales and market states. In the long term, heightened attention to climate risks attenuates the correlation, decreasing integration and amplifying diversification benefits. In the short term, however, the relationship is weaker and primarily influenced by market conditions. This indicates that decoupling occurs during downturns, whereas stronger coupling is observed in bullish or stable markets. Similar results are observed for global risk factors, including crude oil market volatility, financial stress, and investor sentiment. Our findings provide several actionable insights for policy and managerial strategies involving resilience, portfolio risk management during extreme events, investment horizon planning, market integration, and the impacts of policy and regulation.

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