Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors

• Published in: Expert systems with applications Vol. 84; pp. 290 - 300
• Main Authors: Kristjanpoller R, Werner, Hernández P, Esteban
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 30-10-2017; Elsevier BV
• Subjects: ANN-GARCH; Artificial neural networks; Autoregressive processes; Commodity prices; Copper; Currencies; Economic forecasting; GARCH models; Gold; Markets; Mathematical models; Predictions; Regression analysis; Silver; Stock market indexes; Volatility; Volatility prediction; United States > US; GARCH models; Gold; Silver; ANN-GARCH; Volatility prediction; Copper
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.05.024

•A hybrid model is analyzed to predict the price volatility of gold, silver and copper•The hybrid model used is a ANN-GARCH model with regressors.•APGARCH with exogenous variables is used as benchmark.•The benchmark is better than the classical GARCH used in previous studies.•The incorporation of ANN into the best Garch with regressors increases the accuracy.
In this article, we analyze volatility forecasts associated with the price of gold, silver, and copper, three of the most important metals in the world market. First, a group of GARCH models are used to forecast volatility, including explanatory variables like the US Dollar-Euro and US Dollar-Yen exchange rates, the oil price, and the Chinese, Indian, British, and American stock market indexes. Subsequently, these model predictions are used as inputs for a neural network in order to analyze the increase in hybrid predictive power. The results obtained show that for these three metals, using the hybrid neural network model increases the forecasting power of out-of-sample volatility. In order to optimize the results, we conducted a series of sensitizations of the artificial neural network architecture and analyses for different cases, finding that the best models to forecast the price return volatility of these main metals are the ANN-GARCH model with regressors. Due to the heteroscedasticity in the financial series, the loss function used is Heteroskedasticity-adjusted Mean Squared Error (HMSE), and to test the superiority of the models, the Model Confidence Set is used.