Volatility forecasting of clean energy ETF using GARCH-MIDAS with neural network model

Abstract

This paper utilizes a hybrid model to analyze the impression of information from the GECON indicator on the volatility prediction of the clean energy market. The model architecture is constructed by embedding a recurrent neural network (RNN) into the GARCH-MIDAS model. The results show that RNN-GARCH-MIDAS-GECON achieves optimal ranking in volatility prediction. This work confirms the advantages of embedded hybrid integrated models in capturing nonlinear information in financial markets and achieving significant progress in volatility forecasts. Notably, this research will help to promote the construction of clean energy development and energy transition pathways.