Dynamic interactions between green cryptocurrencies and ESG indices: a Multivariate Quantile-on-Quantile Regression approach

Abstract

This study examines the dynamic, asymmetric, and regime-dependent interactions between green cryptocurrencies and ESG indices under external uncertainty. Using an integrated framework combining Time-Varying Parameter Vector Autoregression (TVP-VAR), Multivariate Quantile-on-Quantile Regression (M-QQR), Markov-Switching models, and Two-Stage Least Squares (2SLS), we show that ESG–crypto co-movements are highly conditional. Connectedness intensifies during periods of elevated market volatility, while remaining weaker in tranquil regimes. Financial uncertainty, proxied by the VIX, consistently amplifies ESG–crypto linkages, whereas geopolitical risk (GPR) exerts weaker and more heterogeneous effects. Green cryptocurrencies (ADA, XLM, XNO, XRP, and IOTA) exhibit limited static integration with ESG indices but display strong procyclical alignment in lower return quantiles, challenging their safe-haven role during systemic stress. Regime-switching and 2SLS results confirm robustness and rule out endogeneity. These findings offer important implications for ESG-oriented investors, policymakers, and risk managers integrating digital assets into sustainable portfolios.