Financial Time Series Volatility Forecast Using Evolutionary Hybrid Artificial Neural Network

Financial time series forecast has been classified as standard problem in forecasting due to its high non-linearity and high volatility in data. Statistical methods such as GARCH, GJR, EGARCH and Artificial Neural Networks (ANNs) based on standard learning algorithms such as backpropagation have been widely used for forecasting time series volatility of various fields. In this paper, we propose hybrid model of statistical methods with ANNs. Statistical methods require assumptions about the market, they do not reflect all market variables and they may not capture the non-linearity. Shortcoming of ANNs is their process of identifying inputs insignificantly through which network produces output. The attempt for hybrid system is to outperform the forecast results and overcome the shortcomings by extracting input variables from statistical methods and include them in ANNs learning process. Further genetic algorithm is used for evolution of proposed hybrid models. Experimental results confirm the lesser root mean square error (RMSE) results obtained from proposed evolutionary hybrid ANN models EANN-GARCH, EANN-GJR, EANN-EGARCH than conventional ANNs and statistical methods.