Dynamic q-dependent cross-correlation test for investment classification and its application on green finance

Abstract

This study develops a novel methodological framework, the dynamic $q$-dependent cross-correlation (DQCC) test, to evaluate the diversification, hedging, and safe-haven properties of financial assets under conditions of fractality and nonlinear dependence. Traditional econometric approaches often fail to capture three critical aspects of financial markets: time-varying structures, heterogeneous investment horizons, and fluctuation-dependent dynamics. To address these limitations, the proposed framework integrates fractal theory and econophysics-based cross-correlation measures, enabling a simultaneous analysis of time, scale, and moment dimensions. Empirically, the model is applied to the nexus between green bonds and equity markets. Methodologically, both quantile-based and event-based specifications are employed to assess asset behavior under normal conditions and during systemic crises, including the COVID-19 pandemic, the Russia–Ukraine war, and the Hamas–Israel conflict. The results reveal strong evidence of multifractality and nonlinear dynamics across all return series, rejecting market efficiency. Green bonds are found to provide persistent diversification benefits against both advanced and emerging market equities under ordinary conditions, while their safe-haven properties emerge during extreme downturns, particularly at lower quantiles and longer horizons. Event-based results confirm the safe-haven role of green bonds during COVID-19, with more mixed evidence during geopolitical crises. No robust hedging capacity is observed. The study contributes to the literature by introducing a comprehensive testing framework for investment classification under fractal dynamics and by extending the understanding of the green bond–equity nexus across advanced and emerging markets. The findings carry significant implications for portfolio construction, risk management, and sustainable finance, and the model can be applied to other asset classes to evaluate their potential roles as diversifiers, hedges, or safe-haven.