Forecasting stock volatility process using improved least square support vector machine approach

Considering that the stock returns distribution displays leptokurtosis as well as left-skewed properties, and the returns volatility process exhibits heteroscedasticity as well as clustering effects, the asymmetric GARCH-type models with non-Gaussian distributions (AGARCH-nG) are employed to describe the volatility process. In addition, the AGARCH-nG models are hybridized with artificial neural network (ANN) technique for forecasting stock returns volatility. Since the least square support vector machine (LS-SVM) technique displays strong forecast ability, we present an improved particle swarm optimization (IPSO) algorithm to optimize the parameters of LS-SVM technique in the process of stock returns volatility prediction. Then, we compare the forecasting performances of individual AGARCH-nG models, the hybrid AGARCH-nG-ANN methods and the data mining-based LS-SVM-IPSO method using stock markets data. The empirical results verify the effectiveness and superiority of the proposed method, which demonstrates that the LS-SVM-IPSO approach outperforms the AGARCH-type models with non-Gaussian distributions and those integrating with the artificial neural network methods.