DocumentCode
2853373
Title
Long-Term Memory in Realized Volatility: Evidence from Chinese Stock Market
Author
Cao, Shi-nan ; Li, Han-dong ; Wang, Yan
Author_Institution
Sch. of Manage., Beijing Normal Univ., Beijing, China
fYear
2010
fDate
13-15 Aug. 2010
Firstpage
323
Lastpage
327
Abstract
In this paper, we examine the long-term memory in realized volatility with different time scales based on high frequency data of Shanghai Stock Exchange Composite Index (SSECI). We choose R/S analysis method to calculate Hurst exponents of long-term memory, and use ARFIMA model to estimate and forecast. Our results show that long-term memory in realized volatility becomes strong as time scales increases. We also found that the realized volatility is best measured and forecasted by one-minute interval.
Keywords
autoregressive moving average processes; stock markets; ARFIMA model; Chinese stock market; Hurst exponents; R/S analysis method; Shanghai stock exchange composite index; autoregressive fractional integrated moving average model; long-term volatility memory; realized volatility; Biological system modeling; Indexes; Mathematical model; Predictive models; Stock markets; Time series analysis; ARFIMA Model; Hurst Exponent; Long-term Memory; Realized Volatility;
fLanguage
English
Publisher
ieee
Conference_Titel
Business Intelligence and Financial Engineering (BIFE), 2010 Third International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-7575-9
Type
conf
DOI
10.1109/BIFE.2010.82
Filename
5621831
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