  Recently artificial neural networks (ANNs) have seen success in volatility
prediction, but the literature is divided on where an ANN should be used rather
than the common GARCH model. The purpose of this study is to compare the
volatility prediction performance of ANN and GARCH models when applied to
stocks with low, medium, and high volatility profiles. This approach intends to
identify which model should be used for each case. The volatility profiles
comprise of five sectors that cover all stocks in the U.S stock market from
2005 to 2020. Three GARCH specifications and three ANN architectures are
examined for each sector, where the most adequate model is chosen to move on to
forecasting. The results indicate that the ANN model should be used for
predicting volatility of assets with low volatility profiles, and GARCH models
should be used when predicting volatility of medium and high volatility assets.
