One of the most used methods to forecast price volatility is the generalized autoregressive conditional heteroskedasticity (GARCH) model. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted to improve forecasting models employing a variety of techniques. In this paper, we extend the field of expert systems, forecasting, and model by applying an Artificial Neural Network (ANN) to the GARCH method generating an ANN–GARCH. The hybrid ANN–GARCH model is applied to forecast the gold price volatility (spot and future). The results show an overall improvement in forecasting using the ANN–GARCH as compared to a GARCH method alone. An overall reduction of 25% in the mean average percent error was realized using the ANN–GARCH. The results are realized using the Euro/Dollar and Yen/Dollar exchange rates, the DJI and FTSE stock market indexes, and the oil price return as inputs. We discuss the implications of the study within the context of the discipline as well as practical applications. 2015 Elsevier Ltd.