Volatility Forecasting Using Support Vector Regression and a Hybrid Genetic Algorithm
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 \((\textit{GARCH})\) 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 \((SVR)\) has been employed to forecast financial volatility. Nevertheless, the quality and stability of the model obtained through \(SVR\) training process depend strongly on the selection of \(SVR\) parameters. Typically, these are tuned by a grid search method \((SVR_{GS})\); 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 \(SVR_{GBC}\) for the financial volatility forecasting problem which selects simultaneously the proper kernel and its parameter values. \(SVR_{GBC}\) 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. \(SVR_{GBC}\) results are compared against \(\textit{GARCH}\left( 1,1\right) \hbox { and }SVR_{GS}\) method. It uses the mean absolute percentage error and directional accuracy functions for measuring quality results. Experimentation shows that, in general, \(SVR_{GBC}\) overcomes quality of \(\textit{GARCH}\left( 1,1\right) \hbox { and }SVR_{GS}\).
volatility forecasting support vector regression genetic algorithm boltzmann selection chaotic number generator parameter optimization