BP算法和对称ARCH类模型对股市波动性预测的实证比较

利用我国深圳股票市场的实际数据,建立了相应的BP算法网络预测模型和ARCH(1),GARCH(1,1)预测模型,分别用来对深成指数每个周末收盘价的波动性进行预测．研究表明,BP算法对样本外观测值的上凸曲线拟合得较好,对下凸曲线的拟合效果较差;ARCH(1)和GARCH(1,1)则反之,其预测曲线对样本外观测值的下凸曲线拟合效果都较好,但对上凸曲线的拟合效果都较差．通过采用6种常用的预测误差统计量:平均误差、平均绝对误差、均方根误差、平均绝对比率误差、Akaike信息准则、Baves信息准则对样本外数据的预测结果进行检验,BP算法的预测效果最好,ARCH(1)模型次之,GARcH(1,1)模型偏差．

Comparison:the volatility forecasting of BP algorithm and symmetric ARCH model to stock market

Three forecasting models, called BP algorithm, ARCH(1) and GARCH(1,1), are established based on the actual data of Shenzhen stock market, China. The proposed three models are respectively used to predict the volatility of the weekly closing price of the composition indexes in Shenzhen Stock Exchange. Furthermore, six common statistical methods of the forecasting error, i.e., mean error (ME), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), Akaike's information criterion (AIC) and Bayesian information criterion (BIC) are used to test the forecasting results of the out-of-sample data. The results show that the forecasting result of BP algorithm is the best, the ARCH(1) model takes the second place and the GARCH(1,1) model is the worst.