基于机器学习的已实现波动率预测

Based on Machine Learning for Realized Volatility Prediction

摘要: 选取上证综指5分钟高频数据，以高频价格序列的强记忆性为切入点，构建基于高频价格序列的长短期记忆模型LSTM。基于已实现波动率(RV)理论计算出真实波动率的预测值，选择了效果优异的随机森林模型、弹性网络模型以及直接对波动率建模的LSTM模型进行对比分析，以找出表现较优的预测模型，以期为深度学习在波动率的预测上提供了新思路。研究发现：基于高频价格序列的LSTM波动率预测模型的预测能力明显优于其他两种模型，充分发挥了长短期记忆模型的优势。

Abstract: Selecting the 5-minute high-frequency data of the Shanghai Composite Index and taking the strong memory of the high-frequency price sequence as the entry point, a Long Short-Term Memory (LSTM) model based on the high-frequency price sequence was constructed. Based on the realized volatility (RV) theory, the predicted values of the real volatility were calculated. The random forest model with excellent results, the elastic network model, and the LSTM model directly modeling the volatility were selected for comparative analysis to identify the better-performing prediction model, with the aim of providing new ideas for deep learning in volatility prediction. It was found that the prediction ability of the LSTM volatility prediction model based on the high-frequency price sequence was significantly better than the other two models, giving full play to the advantages of the long short-term memory model.