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
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;
Conference_Titel :
Business Intelligence and Financial Engineering (BIFE), 2010 Third International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7575-9
DOI :
10.1109/BIFE.2010.82
