Hybrid LSTM-GARCH Framework for Financial Market Volatility Risk Prediction

Abstract

This article explores a method that integrates deep learning with classical econometric models to address the challenge of predicting volatility risk in financial markets. In view of the limitations of traditional economic models in capturing complex financial market relationships, researchers propose a new framework that integrates long short-term memory networks (LSTM) and generalized autoregressive conditional heteroskedasticity models (GARCH). This framework takes full advantage of LSTM's ability to handle long-term dependencies and the GARCH model's advantages in capturing volatility and risk by introducing GARCH model parameters as input variables to the LSTM neural network. The experiment uses the historical data of the Nasdaq 100 Index for verification and compares the prediction effects of different models through a variety of evaluation indicators. The results show that the fusion model significantly outperforms the single LSTM model and other benchmark models in prediction accuracy.