基于EMD-SE-LSTM模型的股指日内已实现波动率预测──以中证500指数为例

由于股指波动率具有非平稳、高嘈杂、非线性等特征，而传统的预测模型在建模时要求数据平稳、线性或近似线性，所以很难精准预测股指波动率。为提高股指波动率的预测效果，采用经验模态分解(EMD)、样本熵(SE)和长短期记忆网络(LSTM)构建的模型对股指日内已实现波动率进行预测。以中证500指数为例，经过EMD分解得到一系列分量，再根据分量的样本熵大小进行重构，最后利用LSTM对重构后的各序列进行预测。结果表明，EMD算法对LSTM模型的预测精度有很大的提升，相较于传统模型，EMD-SE-LSTM模型在预测股指波动率时精度更高，拟合优度更好。

Intraday Realized Volatility Forecasting for Stock Indices Based on EMD-SE-LSTM Model:The CSI 500 index as an example

It is difficult to predict stock index volatility accurately because of its non-stationary, highly noisy and non-linear, while traditional forecasting models require smooth, linear or approximately linear data in modeling. To improve the forecasting effect of stock index volatility, a model constructed by empirical modal decomposition (EMD), sample entropy (SE) and long short-term memory network (LSTM) is used to forecast the intra-day realized volatility of stock index. Taking the CSI 500 index as an example, a series of components are obtained after EMD decomposition, and are reconstructed according to the sample entropy magnitude of the components, and finally the LSTM is used to forecast each reconstructed series. The results show that the EMD algorithm also improves the prediction accuracy of the LSTM model, and the EMD-SE-LSTM model has higher accuracy and better fit superiority in predicting the stock index volatility compared with the traditional model.