Forecasting green bond volatility via novel heterogeneous ensemble approaches

Abstract

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.