Using the generalized spillover definition and measurement proposed by Diebold and Yilmaz(2012), we examine the characteristics of daily return and volatility spillovers across 22sectors in Chinese stock market. In static full-sample analysis, we find that more than 90%of forecast error variance comes from spillovers, both for returns (93.30%) and volatilities(92.80%). In time-varying rolling-sample analysis, we come up with striking evidence ofdivergent behavior in the dynamics of return and volatility spillovers. The moving spilloverplots against Shanghai Composite Index (SCI) show clear bursts associated with the globalfinancial crisis 2007-2008. However, they display no bursts but synchronizing trend afterward,presumably due to the step-by-step global financial market integration, and the maturingChinese capital market. The simple correlation coefficients between return spillovers and SCI,and volatility spillovers and SCI are -0.56 and –0.38, respectively.
Using the generalized spillover definition and measurement proposed by Diebold and Yilmaz(2012), we examine the characteristics of daily return and volatility spillovers across 22sectors in Chinese stock market. In static full-sample analysis, we find that more than 90%of forecast error variance comes from spillovers, both for returns (93.30%) and volatilities(92.80%). In time-varying rolling-sample analysis, we come up with striking evidence ofdivergent behavior in the dynamics of return and volatility spillovers. The moving spilloverplots against Shanghai Composite Index (SCI) show clear bursts associated with the globalfinancial crisis 2007-2008. However, they display no bursts but synchronizing trend afterward,presumably due to the step-by-step global financial market integration, and the maturingChinese capital market. The simple correlation coefficients between return spillovers and SCI,and volatility spillovers and SCI are -0.56 and –0.38, respectively.
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