The use of GARCH-neural network model for forecasting the volatility of bid-ask spread of the Chinese stock market

The bid-ask spread is an important indicator of the financial market liquidity and efficiency. In this paper, we study the dynamic of spread volatility of 40 constituent stocks of Shenzhen stock exchange Index (SZEI) in Chinese stock market, using GARCH family models. Perfected GARCH model is identified according to AIC and BIC criteria, then a hybrid GARCH-Neural Network (GARCH-NN) model based on it is proposed to forecast the volatility of SZEI bid-ask spread. Forecast performances are obtained by the perfected GARCH model is compared with that in GARCH-NN based on the SZEI bid-ask spread dataset. The results show that the hybrid model we propose makes better results.