Volatility modeling and forecasting based on high frequency extreme value data

被引：0

作者：

Liu W. ^{[1
]}

Jiang H. ^{[1
]}

Zhang T. ^{[2
]}

Chen W. ^{[3
]}

机构：

[1] School of Finance, Capital University of Economics and Business, Beijing

[2] J. Mack Robinson College of Business, Georgia State University, Atlanta

[3] School of Management Engineering, Capital University of Economics and Business, Beijing

来源：

Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2020年 / 40卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Dynamic forecast; High-frequency extreme value data; Jumps; Volatility;

D O I：

10.12011/SETP2019-2691

中图分类号：

学科分类号：

摘要：

This paper systematically studies the modeling and forecasting of volatility under three types of high-frequency extreme value data, i.e. high-frequency closing price data, high-frequency high-low price data, and high-frequency OHLC data, based on which the theoretical properties of corresponding estimators under continuous price assumptions and under price jump assumptions are discussed and refined, and these estimation methods are uniformly extended to the corresponding dynamic forecasting models. Through the empirical analysis based on the high-frequency data of the Shanghai Stock Index and other major indexes, it reveals that sufficiently utilizing high-frequency extreme data information can significantly improve the model fitting ability and dynamic forecasting ability of volatility. © 2020, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

引用

页码：3095 / 3111

页数：16

共 48 条

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