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 NetworksReferences
Bank of Thailand. (2015). Financial Markets Statisics. Retrieved August 15, 2015, from https://www.bot.or.th/Thai/
Statistics/FinancialMarkets/Pages/default.aspx
Busababodin, P., Siriboon, M. & Kaewmun, A. (2015). Modeling of Extreme Precipitation in Upper Northeast of
Thailand. Burapha Science Journal, 20(1), 106-117. (in Thai)
Dahlan, I., Ahmad, Z., Fadly, M., Lee, K.T., Kamaruddin, A.H. & Mohamed, A.R. (2010). Parameters optimization
of rice husk ash (RHA)/CaO/CeO2 sorbent for predicting SO2/NO sorption capacity using response
surface and neural network models. J. Hazard. Mater, 178, 249-257.
Gold Traders Association. (2015). Features and Benefits of Gold. Retrieved October 20, 2015, from http://www.
goldtraders.or.th/PageView.aspx?page=5
Kaewmart, W. & Chancharat, S. (2013). Artificial Neural Network Forecasting for Stock Indices. KKU Research
Journal (Graduate Studies), 1(1), 108-118. (in Thai)
Kara, Y., Boyacioglu, M.A. & Baykan, K.Ö. (2011). Predicting direction of stock price index movement using
artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange.
Expert Systems with Applications, 38, 5311–5319.
Keerativibool, W. & Na-laed, D. (2013). Forecasting Model of Monthly Gold Ornament Selling Prices. NU Science
Journal, 9(2), 65-81. (in Thai)
Lewis, C.D. (1982). International and Business Forecasting Methods. London: Butterworths.
Liao Z. & Wang J. (2008). Forecasting Model of Chinese Stock Index by Stochastic Time Effective Neural
Network. In Internal Conference on Wireless Communication, Networking and Mobile Computing.
(pp. 1-4). China: IEEE.
Lin, C. (2015). Build Prediction Models for Gold Prices Based on Back-Propagation Neural Network. In
Internal Conference on Modelling Simulation and Appiled Mathematice. (pp. 155-158). Thailand:
Atlantis Press.
Manmin, M. (2006). Time Series and Forecasting. Bangkok: Four Printing Co., Ltd. (in Thai)
Nam, K. & Schaefer, T. (1995). Forecasting international airline passenger traffic using neural networks. Logistics
and Transportation, 31(3), 239-251.
Sonia, R.B. (2015). Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH
frameworks: New evidence. Physica A: Statistical Mechanics and its Applications, 438, 355-364.
Srikaew, A. (2009). Computational Intelligence. Bangkok: Charansnitwong Co., Ltd. (in Thai)
The Stock Exchange of Thailand. (2015). SET Index Series. Retrieved August 15, 2015, from http://www.set.or.th/
th/products/index/setindex_p1.html
U.S. Energy Information Administration. (2015). PETROLEUM & OTHER LIQUIDS. Retrieved August 15, 2015,
from http://tonto.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RWTC&f=D
Zhang, G., Eddy, P.B. & Michael, Y.H. (1998). Forecasting with artificail neural networks: The state of
the art. International Journal of Forecasting, 14, 35-62.
Statistics/FinancialMarkets/Pages/default.aspx
Busababodin, P., Siriboon, M. & Kaewmun, A. (2015). Modeling of Extreme Precipitation in Upper Northeast of
Thailand. Burapha Science Journal, 20(1), 106-117. (in Thai)
Dahlan, I., Ahmad, Z., Fadly, M., Lee, K.T., Kamaruddin, A.H. & Mohamed, A.R. (2010). Parameters optimization
of rice husk ash (RHA)/CaO/CeO2 sorbent for predicting SO2/NO sorption capacity using response
surface and neural network models. J. Hazard. Mater, 178, 249-257.
Gold Traders Association. (2015). Features and Benefits of Gold. Retrieved October 20, 2015, from http://www.
goldtraders.or.th/PageView.aspx?page=5
Kaewmart, W. & Chancharat, S. (2013). Artificial Neural Network Forecasting for Stock Indices. KKU Research
Journal (Graduate Studies), 1(1), 108-118. (in Thai)
Kara, Y., Boyacioglu, M.A. & Baykan, K.Ö. (2011). Predicting direction of stock price index movement using
artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange.
Expert Systems with Applications, 38, 5311–5319.
Keerativibool, W. & Na-laed, D. (2013). Forecasting Model of Monthly Gold Ornament Selling Prices. NU Science
Journal, 9(2), 65-81. (in Thai)
Lewis, C.D. (1982). International and Business Forecasting Methods. London: Butterworths.
Liao Z. & Wang J. (2008). Forecasting Model of Chinese Stock Index by Stochastic Time Effective Neural
Network. In Internal Conference on Wireless Communication, Networking and Mobile Computing.
(pp. 1-4). China: IEEE.
Lin, C. (2015). Build Prediction Models for Gold Prices Based on Back-Propagation Neural Network. In
Internal Conference on Modelling Simulation and Appiled Mathematice. (pp. 155-158). Thailand:
Atlantis Press.
Manmin, M. (2006). Time Series and Forecasting. Bangkok: Four Printing Co., Ltd. (in Thai)
Nam, K. & Schaefer, T. (1995). Forecasting international airline passenger traffic using neural networks. Logistics
and Transportation, 31(3), 239-251.
Sonia, R.B. (2015). Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH
frameworks: New evidence. Physica A: Statistical Mechanics and its Applications, 438, 355-364.
Srikaew, A. (2009). Computational Intelligence. Bangkok: Charansnitwong Co., Ltd. (in Thai)
The Stock Exchange of Thailand. (2015). SET Index Series. Retrieved August 15, 2015, from http://www.set.or.th/
th/products/index/setindex_p1.html
U.S. Energy Information Administration. (2015). PETROLEUM & OTHER LIQUIDS. Retrieved August 15, 2015,
from http://tonto.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RWTC&f=D
Zhang, G., Eddy, P.B. & Michael, Y.H. (1998). Forecasting with artificail neural networks: The state of
the art. International Journal of Forecasting, 14, 35-62.
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Published
2017-01-31
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