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Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, more accurate measures and better forecasts of volatility are crucial for the implementation and evaluation of asset and derivative pricing models in addition to trading and hedging strategies. However, whilst GARCH models are able to capture the observed clustering effect in asset price volatility, they appear to provide relatively poor out-of-sample forecasts. In order to improve volatility forecasts, we put forward three combining volatility forecasting methods through simple averaging, an ordinary least squares model and an artificial neural network using daily closing data of the Shanghai Stock Exchange Composite index. The empirical study reveals that combining volatility forecasting methods have better forecast performance, and the non-linear method simulated by neural network are especially superior.