Dynamic Long Memory High Frequency Multipower Variation Volatility Evaluations for S&P500
Wen Cheong Chin,
Min Cherng Lee,
Tan Pei Pei,
Grace Lee Ching Yap and
ChristineTan Nya Ling
Modern Applied Science, 2016, vol. 10, issue 5, 1
Abstract:
This study explores the multipower variation integrated volatility estimates using high frequency data in financial stock market. The different combinations of multipower variation estimators are robust to drastic financial jumps and market microstructure noise. In order to examine the informationally market efficiency, we proposed a rolling window estimate procedures of Hurst parameter using the modified rescale-range approach. In order to test the robustness of the method, we have selected the S&P500 as the empirical data. The empirical study found that the long memory cascading volatility is fluctuating across the studied period and drastically trim down after the subprime mortgage crisis. This time-varying long memory analysis allow us to understand the informationally market efficiency before and after the subprime mortgage crisis in U.S.
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://ccsenet.org/journal/index.php/mas/article/download/52267/30588 (application/pdf)
https://ccsenet.org/journal/index.php/mas/article/view/52267 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ibn:masjnl:v:10:y:2016:i:5:p:1
Access Statistics for this article
More articles in Modern Applied Science from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().