Author :
Abstract
This study analyzes the connectedness dynamics between artificial intelligence (AI)-themed indices and the global environmental, social, and governance (ESG) index within a nonlinear and regime-sensitive framework. Using daily data for the 2018-2025 period, the Quantile-on-Quantile Connectedness (QQC) approach is employed to examine how information transmission between AI and ESG markets varies across different distributional states. The empirical findings indicate that under adverse market regimes, AI indices predominantly act as receivers of information, while the ESG index assumes a transmitter role. In contrast, during periods associated with more favorable market conditions, the direction of information flow reverses, and AI indices tend to function as net transmitters influencing ESG performance. These results reveal that the connectedness between AI and ESG indices is highly nonlinear, asymmetric, and strongly dependent on market regimes rather than being stable over time. Overall, the findings provide relevant insights for investors, policymakers, and financial regulators by highlighting how AI and sustainability-oriented markets alternately shape information flows under stress and non-stress conditions, thereby offering a regime-aware framework for portfolio diversification, risk monitoring, and financial stability assessment.
Keywords
Abstract
This study analyzes the connectedness dynamics between artificial intelligence (AI)-themed indices and the global environmental, social, and governance (ESG) index within a nonlinear and regime-sensitive framework. Using daily data for the 2018-2025 period, the Quantile-on-Quantile Connectedness (QQC) approach is employed to examine how information transmission between AI and ESG markets varies across different distributional states. The empirical findings indicate that under adverse market regimes, AI indices predominantly act as receivers of information, while the ESG index assumes a transmitter role. In contrast, during periods associated with more favorable market conditions, the direction of information flow reverses, and AI indices tend to function as net transmitters influencing ESG performance. These results reveal that the connectedness between AI and ESG indices is highly nonlinear, asymmetric, and strongly dependent on market regimes rather than being stable over time. Overall, the findings provide relevant insights for investors, policymakers, and financial regulators by highlighting how AI and sustainability-oriented markets alternately shape information flows under stress and non-stress conditions, thereby offering a regime-aware framework for portfolio diversification, risk monitoring, and financial stability assessment.
