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Forecasting security’s volatility using low-frequency and high-frequency historical data and option-implied information

时间:2018-03-16

Statistics Seminar2018-02


Topic: Forecasting security’s volatility using low-frequency and high-frequency historical data and option-implied information

Speaker: Xiangyu Cui, Shanghai University of Finance and Economics

Time: Thursday, March 22,14:00-15:00

Place: Room 217, Guanghua Building 2


Abstract:

Low-frequency historical data, high-frequency historical data and option data are three major sources of forecasting the underlying security’s volatility. In this paper, we propose a unified GARCH-Ito-OI model to integrate three information sources. Instead of using options’ price data directly, we extract the option-implied information, such as implied volatility, from the option data and consider it as an exogenous variable. We provide the quasi-maximum likelihood estimators for the parameters and establish the asymptotic theory for the estimators. The empirical analysis shows that the proposed GARCH-Ito-OI model has better out-of-sample forecasting performances than the models, which rely on two information sources.


Introduction:

崔翔宇,上海财经大学统计与管理学院副教授,博士生导师。中国科技大学学士,硕士。香港中文大学博士。主要研究兴趣为数量金融,风险管理,行为金融。在《Operations Research》,《Mathematical Finance》,《Journal of Economic Dynamics and Control》,《European Journal of Operational Research》等国外著名学术期刊发表论文17篇。主持国家自然科学基金两项。

http://ssm.shufe.edu.cn/Home/Index/teacher?id=66


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