Abstract
The unique characteristics of Chinese stock markets give rise to the difficulty of assuming innovation distributions and the specification form of the volatility process when modelling volatility with the parametric GARCH family models. This paper examines the Chinese stock market volatility and the asymmetric effects in the Chinese stock volatility by a generalized additive nonparametric (GAM NP) smoothing technique. The empirical results indicates that the asymmetric effect exists in the Chinese stock market. However, the News Impact Curve from the GAM NP model indicates that limited amounts of good news are needed in order to to keep the market calm. Further, compared with other parametric and nonparametricmodels, the empirical result from the GAMNP model demonstrates better performance for the volatility forecasts, particularly in the out-of-sample forecast. This GAM NP technique should have wide applications to other merging stock markets which are similarly imperfect and incomplete.
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