Green investing is becoming popular in India as the government aims to reduce CO2 emissions through eco-friendly projects. However, these investments in Indian green companies are new and may face price fluctuations and risks linked to other assets. To better understand these risks, we employ a dynamic conditional correlation– generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model, enabling us to analyze market interdependence by estimating time-varying conditional correlation using monthly data. Our study focuses on the volatility spillover effects within Indian sustainability indexes (S&P BSE CARBONEX and S&P BSE GREENEX), which can be predicted based on information regarding the market volatility of traditional stocks, crude oil and economic policy uncertainty. We show that there is high persistence in the conditional variances for all pairs, indicating that past shocks to volatility have a long-lasting impact on future volatility. However, the effects of ARCH are found to be weak, suggesting that recent volatility innovations have a limited influence on future volatility. The DCC persistence is found to be significantly positive for all pairs, indicating a strong dynamic dependence between the volatilities of pairs of assets. Understanding these dynamics can guide both portfolio diversification for investors and the crafting of effective policies.