A hybrid modeling approach for forecasting the volatility of S&P 500 index return

Forecasting volatility is an essential step in many financial decision makings. GARCH family of models has been extensively used in finance and economics, particularly for estimating volatility. The motivation of this study is to enhance the ability of GARCH models in forecasting the return volatility. We propose two hybrid models based on EGARCH and Artificial Neural Networks to forecast the volatility of S&P 500 index. The estimates of volatility obtained by an EGARCH model are fed forward to a Neural Network. The input to the first hybrid model is complemented by historical values of other explanatory variables. The second hybrid model takes as inputs both series of the simulated data and explanatory variables. The forecasts obtained by each of those hybrid models have been compared with those of EGARCH model in terms of closeness to the realized volatility. The computational results demonstrate that the second hybrid model provides better volatility forecasts.

► GARCH models have been complemented with Neural Networks to better model and forecast volatility in highly fluctuating market.
► The volatility estimates, other explanatory variables and simulated volatility series have been used as inputs to characterize the statistical properties of the volatility series.
► The computational results on S&P 500 show that such hybrid models provide significantly better volatility forecasts.
► Further applications of such volatility forecasts in various financial engineering problems would be interesting.