Forecasting Volatility of Gold Price with Artificial Neural Networks
Abstract
This study aimed to develop a gold price volatility forecasting model by artificial neuron networks with Thailand daily maximum gold bar price data from May 2, 2006 to September 30, 2015 (2,788 days). Data were divided into two groups, 90% of data (2,509 days) from May 2, 2006 until October 24, 2014 were used as a training the network and another 10% of data (279 days) were used to test the model’s effectiveness and accuracy. The criterions as mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were also calculated for the accuracy. The artificial neuron networks via Backpropagation learning with one hidden layer and 20 nodes performed the lowest MAE, RMSE, and MAPE.
Keywords: Volatility of Gold Price, Artificial Neural Networks
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