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Do Chinese stock markets share common information arrival processes?

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Author Info
Philip Kostov (Queen's University Belfast)
Ziping Wu (Queen's University Belfast)
Seamus McErlean (Queen's University Belfast)

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Abstract

According to the Mixture of Distributions Hypothesis (MDH), returns volatility and trading volume are driven by a common news arrival variable. Consequently, these two variables should be correlated. This paper extends, and to some extent, globalises the concept of a common information arrival process by hypothesising that this variable drives daily price (returns) volatility and trading volume changes in different financial markets. An implication is that returns volatility in one stock market should show positive and contemporaneous correlation with returns volatility in another stock market. This paper tests this implication using data from three separate, but geographically close, stock markets (Shenzhen, Shanghai and Hong Kong). A problem in the usual testing procedure is the likelihood that the news arrival process has long memory. This means that both volatility and volume (or external volatility) will have long memory and consequently, contemporaneous correlation between these variables is likely to be incorrectly rejected in cases where the test equation does not account for long memory. This paper uses fractionally integrated GARCH (FIGARCH) to test and account for long memory. The analysis finds that there is contemporaneous correlation between returns volatility in these stock markets and confirms the presence of long memory effects.

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Publisher Info
Paper provided by EconWPA in its series Econometrics with number 0410001.

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Length: 33 pages
Date of creation: 01 Oct 2004
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Handle: RePEc:wpa:wuwpem:0410001

Note: Type of Document - pdf; pages: 33
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Web page: http://129.3.20.41

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Related research
Keywords: mixture of distributions hypothesis; news arrival process; FIGARCH; volatility; long memory;

Find related papers by JEL classification:
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General
C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
C5 - Mathematical and Quantitative Methods - - Econometric Modeling
C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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    Other versions:
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    Other versions:
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