Integrated prediction of green bond return under the dual risks of climate change and energy crisis - Nie, Qimiao; Chen, Siying; Chen, Yiming; Hu, Yiguo (Frontiers in Environmental Science, 2023)
Prediction of bond return is a classic problem in financial area, providing an important basis for portfolio construction and risk management. The sustainable investment attribute of green bonds has been favored by investors, so that green bonds have become an important component for major asset allocation. However, due to the specific investment focus of green bonds, investors' return expectations are influenced not only by traditional corporate bond factors, but also by related factors such as climate change and energy transition. Against the backdrop of increasingly severe climate risks and the global energy crisis, this paper analyses the volatility characteristics of China's green bonds at multiple time scales, and introduces exogenous variables such as returns of the alternative financial assets, climate risks and returns of energy markets for prediction. Based on the LSTM model, the volatility of green bond yield at different time scales is separately predicted using optimal exogenous variable before integration. It is found that the new integrated prediction model can significantly improve the forecasting performance compared to traditional single LSTM models and simple decompositionintegrated models. Further, both climate risks and energy markets variables have a significant improvement effect on predicting green bond in low-frequency item, while energy markets variables also have a better predictive effect on trend items. Building on the use of only LSTM model, it could be further enhanced by integrating more algorithms to select the best single model for each component, further improve the prediction accuracy and provide a more effective quantitative tool for investment decision-making and risk management in related fields.