English, Article edition: LONG-TERM MEMORY IN EMERGING MARKETS: EVIDENCE FROM THE CHINESE STOCK MARKET CHAOQUN MA; HONGQUAN LI; LIN ZOU; ...

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Physical Description
  • article
Language
  • English

Edition details

Title
  • LONG-TERM MEMORY IN EMERGING MARKETS: EVIDENCE FROM THE CHINESE STOCK MARKET
Author
  • CHAOQUN MA
  • HONGQUAN LI
  • LIN ZOU
  • ZHIJIAN WU
Physical Description
  • article
Notes
  • The notion of long-term memory has received considerable attention in empirical finance. This paper makes two main contributions. First one is, the paper provides evidence of long-term memory dynamics in the equity market of China. An analysis of market patterns in the Chinese market (a typical emerging market) instead of US market (a developed market) will be meaningful because little research on the behaviors of emerging markets has been carried out previously. Second one is, we present a comprehensive research on the long-term memory characteristics in the Chinese stock market returns as well as volatilities. While many empirical results have been obtained on the detection of long-term memory in returns series, very few investigations are focused on the market volatility, though the long-term dependence in volatility may lead to some types of volatility persistence as observed in financial markets and affect volatility forecasts and derivative pricing formulas. By means of using modified rescaled range analysis and Autoregressive Fractally Integrated Moving Average model testing, this study examines the long-term dependence in Chinese stock market returns and volatility. The results show that although the returns themselves contain little serial correlation, the variability of returns has significant long-term dependence. It would be beneficial to encompass long-term memory structure to assess the behavior of stock prices and to research on financial market theory.
  • ARFIMA model, long-term memory, modified rescaled range analysis, stock market
  • RePEc:wsi:ijitdm:v:05:y:2006:i:03:p:495-501
Language
  • English
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