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In this paper, we propose a new MRMRKA-SVR-GARCH model to predict the volatility of time series. This model is a combination of the MRMRKA-SVR model and the GARCH model. Since SVR-GARCH model has been successfully employed to forecast volatility. Empirical results show that the multi-kernel support vector regression based on minimal redundancy maximal correlation criteria and kernel target alignment (MRMRKA-SVR) tend to beat single-kernel SVR models in terms of forecasting accuracy. First, we use the MRMRKA algorithm to select a set of kernels to establish a multi-kernel, and establish the MRMRKA-SVR model. Then we use MRMRKA-SVR to estimate the volatility equation of GARCH model, and establish MRMRKA-SVR-GARCH model. Furthermore, the models’ predictive ability was evaluated using three empirical analyses on China Unicom's stock price data and stock price data of ICBC. Under different basic kernel numbers m, compare the prediction errors between the proposed model and the SVR-GARCH model based on the mixed Gaussian kernels in the two stock data, the results show that the prediction accuracy of the MRMRKA-SVR-GARCH model fluctuates slightly with the change of m , and the MAE and RMSE of the MRMRKA-SVR-GARCH model are lower than the mixed-Gaussian-kernel SVR-GARCH model. Description MRMRKA-SVR-GARCH model managed to outperform the mixed-Gaussian-kernel SVR-GARCH model. Therefore, the MRMRKA-SVR-GARCH model can provide a certain degree of reference in analyzing the trend and prediction of volatility, and has high feasibility and effectiveness.
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