Volatility forecasting is an important process required to measure variability in equity prices, risk management, and several other financial activities. Generalized autoregressive conditional heteroscedastic methods (GARCH)\documentclass[12pt]{minimal}
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\begin{document}$$(\textit{GARCH})$$\end{document} have been used to forecast volatility with reasonable success due unreal assumptions about volatility underlying process. Recently, a supervised learning machine called support vector regression (SVR)\documentclass[12pt]{minimal}
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\begin{document}$$(SVR)$$\end{document} has been employed to forecast financial volatility. Nevertheless, the quality and stability of the model obtained through SVR\documentclass[12pt]{minimal}
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\begin{document}$$SVR$$\end{document} training process depend strongly on the selection of SVR\documentclass[12pt]{minimal}
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\begin{document}$$SVR$$\end{document} parameters. Typically, these are tuned by a grid search method (SVRGS)\documentclass[12pt]{minimal}
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\begin{document}$$(SVR_{GS})$$\end{document}; however, this tuning procedure is prone to get trapped on local optima, requires a priori information, and it does not concurrently tune the kernels and its parameters. This paper presents a new method called SVRGBC\documentclass[12pt]{minimal}
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\begin{document}$$SVR_{GBC}$$\end{document} for the financial volatility forecasting problem which selects simultaneously the proper kernel and its parameter values. SVRGBC\documentclass[12pt]{minimal}
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\begin{document}$$SVR_{GBC}$$\end{document} is a hybrid genetic algorithm which uses several genetic operators to enhance the exploration of solutions space: it introduces a new genetic operator called Boltzmann selection, and the use of several random number generators. Experimental data correspond to two ASEAN and two latinoamerican market indexes. SVRGBC\documentclass[12pt]{minimal}
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\begin{document}$$SVR_{GBC}$$\end{document} results are compared against GARCH1,1andSVRGS\documentclass[12pt]{minimal}
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\begin{document}$$\textit{GARCH}\left( 1,1\right) \hbox { and }SVR_{GS}$$\end{document} method. It uses the mean absolute percentage error and directional accuracy functions for measuring quality results. Experimentation shows that, in general, SVRGBC\documentclass[12pt]{minimal}
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\begin{document}$$SVR_{GBC}$$\end{document} overcomes quality of GARCH1,1andSVRGS\documentclass[12pt]{minimal}
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\begin{document}$$\textit{GARCH}\left( 1,1\right) \hbox { and }SVR_{GS}$$\end{document}.
