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基于XGBoost和HAR-RV的已实现波动率预测
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Abstract:
本研究构建了基于极端梯度提升(XGBoost)和已实现波动率异质自回归(HAR-RV)的混合模型，采用沪深300指数的五分钟价格数据，选取已实现波动率的历史值、市场交易指标和技术指标作为特征进行预测，根据XGBoost重要性评分，使用递归特征消除法进行特征选择。实验结果表明，我们所提出的混合模型预测效果优于目前主流应用的单一模型，XGBoost递归特征消除起到了优化特征子集的作用。本研究旨在为金融市场的波动率预测提供新的视角，并为投资者和风险管理者提供一种有效的工具。
This study develops a hybrid model based on Extreme Gradient Boosting (XGBoost) and Heterogeneous Autoregression of Realized Volatility (HAR-RV), employing five-minute price data from the CSI 300 Index. We select historical values of realized volatility, trading indicators, and technical indicators as features for prediction. Feature selection is conducted using Recursive Feature Elimination based on XGBoost importance scores. The experimental results indicate that the hybrid model we propose has superior predictive performance compared to the currently mainstream single models, and the XGBoost recursive feature elimination effectively optimizes the subset of features. This research aims to provide a fresh perspective on financial market volatility prediction and to offer investors and risk managers a potent tool.
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