Green　bonds　are　powerful　tools　for　fighting　against　climate　change　and　typically　exhibit　more　volatility　than　conventional　bonds　do.　However,　the　volatility　forecasting　of　green　bond　has　received　little　attention　in　previous　literature.　This　study　proposes　two　novel　heterogeneous　ensemble　models,　which　differ　from　common　volatility　forecasting　in　that　they　are　combine　advanced　tree-based　ensemble　models　and　exogenous　predictors　from　other　financial　and　commodity　markets　to　forecast　the　volatility　of　green　bonds.　Validated　on　multiple　green　bonds　indexes,　loss　functions,　and　time　horizons,　the　comparative　results　show　that　the　incorporation　of　exogenous　predictors　can　enhance　the　predictive　accuracy　of　volatility　forecasting　models,　which　is　also　confirmed　by　the　marginal　effects　illustrated　by　SHapley　Additive　exPlanations　(SHAP)　values.　The　proposed　EX-SEL　model　significantly　outperforms　the　benchmark　models　in　most　cases.　The　results　of　the　robustness　check　further　indicate　that　the　empirical　results　are　robust　to　alternative　volatility　estimators,　extreme　events　such　as　the　COVID-19　pandemic,　and　alternative　selection　strategies.