报告题目：Forecasting the high frequency volatility based on the LSTM-HIT model
报告人：刘广应（南京审计大学）
邀请人：宗高峰副教授
主持人：安起光教授 统计与数学学院院长
报告地点：舜耕校区4号教学楼315会议室
报告时间：2024年5月24日（周五）15:00-17:00
主办单位：山东财经大学统计与数学学院
摘要：Volatility forecasting from high-frequency data plays a crucial role in many financial fields, such as risk management, option pricing, and portfolio management. Many of existing statistical models could better describe and forecast the characteristics of volatility, whereas they do not simultaneously account for the long memory of volatility, the nonlinear characteristics of high-frequency data, and technical index information during the modeling phase. The purpose of this paper is to use the prediction advantage of deep learning long short term memory (LSTM) model to predict the volatility fusing three classes of information, i.e., high frequency realized volatility (H), technical indicators (I), and the parameters of GARCH, HAR and other time series models (T), resulting in a novel LSTM-HIT model to forecast realized volatility. We employ extreme value theory (EVT) of a semiparametric method to estimate the quantile of standardized return, and construct the LSTM-HIT-EVT model to forecast the value at risk (VaR). Empirical results show that the LSTM-HIT model provides the most accurate volatility forecast among the various considered models and that the LSTM-HIT-EVT model yields forecasts more accurate than other VaR models.
报告人简介：刘广应，南京审计大学教授，博士生导师，复旦大学博士，浙江大学博士后，香港科技大学访问学者。现为南京审计大学金融数学系主任，江苏省“青蓝工程”学术带头人，中国现场统计研究会旅游大数据学会理事、中国管理科学与工程学会理事、中国管理科学与工程学会金融计量与风险管理分会理事、江苏省概率统计协会理事。研究领域与兴趣：金融高频数据、应用统计、深度学习、金融数学等。在《中国科学》《Journal of the American Statistical Association》《Journal of Econometrics》《Journal of Business & Economic Statistics》等国内外杂志发表或录用论文30多篇。主持2项国家自然科学基金项目、1项国家社会科学基金项目、多项省部级课题。