The interaction and volatility asymmetry of unexpected returns in the greater China stock markets

Yin-Hua Yeh*, Tsun Siou Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Scopus citations


The response of investors to unexpected returns and the information transmission in the stock markets of the Greater China area are investigated in this study. First, we analyze the asymmetric reaction of return volatility to good and bad news by utilizing generalized autoregressive conditional heteroskedasticity (GARCH) model. We find that the impact of bad news (negative unexpected return) on future volatility is greater than the impact of good news (positive unexpected return) of the same magnitude in Taiwan and Hong Kong, consistent with the previous literature. However, just the opposite is found in the Shanghai and Shenzhen markets, implying good-news-chasing behavior of the investors. This phenomenon also indicates that behaviors of the investors in Mainland China may be inclined to support the trading noise hypothesis. Further, this study examines information transmission of contemporaneous and cross-period by exploring the interaction of unexpected returns among these four markets. The results of a near vector autoregressions (VAR) model reveal that the Hong Kong stock market plays a most influential role (regional force) among the Taiwan, Shanghai, and Shenzhen B-share stock markets. Finally, the stock returns in the Taiwan market, which has been quite independent of the Mainland China stock markets, became negatively correlated with the Shanghai B-share market during the Taiwan Strait Crisis period. The interaction among financial markets seems to be strengthened by political incidents.

Original languageEnglish
Pages (from-to)129-149
Number of pages21
JournalGlobal Finance Journal
Issue number1-2
StatePublished - 1 Dec 2000


  • G12
  • G14
  • G15
  • Information transmission
  • Return interaction
  • The Greater China stock markets
  • Volatility asymmetry

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