数据与计算发展前沿2026，Vol.8Issue(1)：45-63,19.DOI:10.11871/jfdc.issn.2096-742X.2026.01.005
跳跃信息、机器学习模型与已实现波动率预测
Jump Information,Machine Learning Models,and Realized Volatility Forecasting
摘要
Abstract
[Objective]This study explores the synergy between stock price jump characteristics and machine learning mod-els in realized volatility forecasting,analyzing the performance of different prediction methods across various time scales.[Methods]Using five-minute high-frequency data of SSE 50 Index constituent stocks from 2019 to 2024,we identify stock price jumps point by point with the threshold method and extract multidimensional fea-tures such as jump frequency and jump magnitude via the K-nearest neighbors(KNN)algorithm,constructing a feature system enriched with jump information.Subsequently,we employ extended Heterogeneous Autoregres-sive(HAR)models and ten machine learning algorithms,including KNN,Random Forest(RF),Gradient Boost-ing Regression Trees(GBRT),and Support Vector Regression(SVR),to predict realized volatility over multiple time horizons and systematically assess the combined effect of machine learning methods and jump information.[Results]In in-sample predictions,incorporating jump features and applying machine learning models both im-prove forecasting accuracy,with KNN and Random Forest performing the best.In out-of-sample predictions,the HAR-RV model is optimal for daily forecasts,while for weekly and monthly forecasts,jump information and ma-chine learning models enhance prediction performance.However,when the HAR model already integrates jump information,machine learning methods fail to provide additional predictive improvements.[Conclusions]This study expands the feature space for volatility forecasting and systematically evaluates the effectiveness of ma-chine learning methods in volatility prediction.The findings indicate that multidimensional jump features provide additional information that enhances medium-to long-term volatility forecasting accuracy.However,machine learning models can hardly further increase incremental value when the HAR model incorporates jump informa-tion.These insights hold significant implications for financial market risk management and asset pricing.关键词
已实现波动率/跳跃/预测/机器学习/高频数据Key words
realized volatility/jumps/forecasting/machine learning/high-frequency data引用本文复制引用
冯文君,张正军,王一鸣..跳跃信息、机器学习模型与已实现波动率预测[J].数据与计算发展前沿,2026,8(1):45-63,19.基金项目
国家自然科学基金重大基金项目(71991471) （71991471）
国家自然科学基金重点基金项目(72442027) （72442027）
国家自然科学基金青年科学基金项目(72401025) （72401025）
北京市国际金融学会研究课题资金(BIFS242001) （BIFS242001）
