崔翔宇 | Forecasting security’s volatility using low-frequency historical data, high-frequency historical data and option-implied volatility

时间：2018年09月10日(周一)上午9:30-10:30
地点：中山北路校区理科大楼A302室
题目：Forecasting security’s volatility using low-frequency historical data, high-frequency historical data and option-implied volatility
报告人：崔翔宇 上海财经大学统计与管理学院
摘要：
Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security’s volatility. In this paper, we propose a unified GARCH-Ito-IV model, which is the first explicit model integrating three information sources in the literature. Instead of using options’ price data directly in the model, we extract the option-implied volatility from the option data and construct its dynamics. We provide the quasi-maximum likelihood estimators for the parameters and establish their asymptotic properties. In empirical analysis, we show that the proposed GARCH-Ito-IV model has better out-of-sample volatility forecasting performance than the popular models, such as GARCH+IV, Realized GARCH(IV), Realized GARCH(RV), HAR-RV and GARCH-Ito.
报告人介绍：
崔翔宇，中国科学技术大学学士，硕士，香港中文大学博士。现为上海财经大学统计与管理学院，常任教职副教授（已获得终身教职），研究员，博导。管理科学与工程学会金融计量和风险管理研究会秘书长，中国管理现代化研究会管理与决策科学专业委员会理事。主要研究领域包括行为金融，数量金融，风险管理，在《Operations Research》，《Mathematical Finance》，《Journal of Economic Dynamics & Control》等国际著名SSCI/SCI期刊发表论文20篇。