Using neural networks for forecasting volatility of S&P 500 Index futures prices

The ability to forecast the volatility of the markets is critical to analysts. Among the large array of approaches available for forecasting volatility, neural networks are gaining in popularity. We present a primer for using neural networks for financial forecasting. We compare volatility forecasts from neural networks with implied volatility from S&P 500 Index futures options using the Barone-Adesi and Whaley (BAW) American futures options pricing model. Forecasts from neural networks outperform implied volatility forecasts and are not found to be significantly different from realized volatility. Implied volatility forecasts are found to be significantly different from realized volatility in two of three forecast horizons.