%0 Journal Article
%T The Intraday Periodicity and Long-memory Characters in High-frequency Data of China Stock Market<br>中国股市高频数据中的周期性和长记忆性
%A TAO Li-bin
%A FANG Zhao-ben
%A PAN Wan-bin
%A <br>陶利斌
%A 方兆本
%A 潘婉彬
%J 系统工程理论与实践
%D 2004
%I 
%X By FFF regression of Andersen and Bollerslev(1997), we analysis the periodicity of Shanghai stock index 5-min high frequency data and the long memory characters in filtered absolute returns. We document that the periodicity of intraday absolute returns is stronger than that of raw intraday returns, and FFF regression is an efficient way of determining the periodicity. After comparing the previous results, we found that the long memory in high-frequency absolute returns is stronger than that of daily returns.
%K periodicity
%K long memory
%K high-frequency data<br>周期性
%K 长记忆性
%K 高频数据
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=01BA20E8BA813E1908F3698710BBFEFEE816345F465FEBA5&cid=962324E222C1AC1D&jid=1D057D9E7CAD6BEE9FA97306E08E48D3&aid=EE34B9532B11713D&yid=D0E58B75BFD8E51C&vid=B91E8C6D6FE990DB&iid=B31275AF3241DB2D&sid=96C778EE049EE47D&eid=9971A5E270697F23&journal_id=1000-6788&journal_name=系统工程理论与实践&referenced_num=6&reference_num=8