LSTM-based volatility prediction study of SSE index

The demand for stock volatility in the financial field is more stringent, this paper tries to use LSTM model to predict the future stock volatility. The stock data of SSE index from 2008 to 2020 are divided into training set and test set. LSTM model is used to predict volatility, and the accuracy of LSTM prediction is good, but the accuracy of peak data is poor. The LSTM model can be modified or combined with other traditional machine learning methods to calculate volatility better, and can be used in the financial field with less manual time.