Volatility Prediction via Hybrid LSTM Models with GARCH Type Parameters

Abstract

Since the establishment of financial models for risk prediction, the measurement of volatility at risky market has improved, and its significance has also grown. For high-frequency financial data, the degree of investment risk, which has always been the focus of attention, is measured by the variance of residual sequence obtained following model regression. By integrating the long short-term memory (LSTM) model with multiple generalized autoregressive conditional heteroscedasticity (GARCH) models, a new hybrid LSTM model is used to predict stock price volatility. In this paper, three GARCH models are used, and the model that can best fit the data is determined.