Volatility modelling and prediction by hybrid support vector regression with chaotic genetic algorithms

In this paper, a new econometric model of volatility is proposed using hybrid Support Vector machine for Regression (SVR) combined with Chaotic Genetic Algorithm (CGA) to fit conditional mean and then conditional variance of stock market returns. The CGA, integrated by chaotic optimization algorithm with Genetic Algorithm (GA), is used to overcome premature local optimum in determining three hyperparameters of SVR model. The proposed hybrid SVRCGA model is achieved, which includes the selection of input variables by ARMA approach for fitting both mean and variance functions of returns, and also the searching process of obtaining the optimal SVR hyperparameters based on the CGA while training the SVR. Real data of complex stock markets (NASDAQ) are applied to validate and check the predicting accuracy of the hybrid SVRCGA model. The experimental results showed that the proposed model outperforms the other competing models including SVR with GA, standard SVR, Kernel smoothing and several parametric GARCH type models.