Modeling volatility of irregularly spaced time series: Union of high-frequency and low-frequency data

被引：0

作者：

Wu B. ^{[1
]}

Zhang B. ^{[1
]}

Zhao L. ^{[2
]}

机构：

[1] Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing

[2] Business College, Yangzhou University, Yangzhou

来源：

Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2019年 / 39卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Bulk volume classification; GARCH structure; High-frequency data;

D O I：

10.12011/1000-6788-2017-1093-13

中图分类号：

学科分类号：

摘要：

This paper extends the unified GARCH-Itô model proposed by Kim and Wang (2016) and introduces a more general method to model volatility with the combination of high-frequency and lowfrequency data. The new method embeds a low-frequency GARCH structure into high-frequency volatility in a more flexible way, thus embraces a broader application. Theory and simulation study found good asymptotic property and finite sample performances of the quasi-maximum likelihood estimators proposed. In an empirical study, this new method was used to improve the bulk volume classification (BVC, Easley et al.(2013, 2016)). As a result, market participants' trading intentions were estimated more precisely. © 2019, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

引用

页码：36 / 48

页数：12

共 20 条

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