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Forecasting the COMEX copper spot price by means of neural networks and ARIMA models

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  • Sánchez Lasheras, Fernando
  • de Cos Juez, Francisco Javier
  • Suárez Sánchez, Ana
  • Krzemień, Alicja
  • Riesgo Fernández, Pedro

Abstract

This paper examines the forecasting performance of ARIMA and two different kinds of artificial neural networks models (multilayer perceptron and Elman) using published data of copper spot prices from the New York Commodity Exchange, (COMEX). The empirical results obtained showed a better performance of both neural networks models over the ARIMA. The findings of this research are in line with some previous studies, which confirmed the superiority of neural networks over ARIMA models in relative research areas.

Suggested Citation

  • Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    • Handle: RePEc:eee:jrpoli:v:45:y:2015:i:c:p:37-43
    DOI: 10.1016/j.resourpol.2015.03.004
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    References listed on IDEAS

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    1. Dooley, Gillian & Lenihan, Helena, 2005. "An assessment of time series methods in metal price forecasting," Resources Policy, Elsevier, vol. 30(3), pages 208-217, September.
    2. Barry A. Goss & S. Gulay Avsar, 2013. "Simultaneity, Forecasting and Profits in London Copper Futures," Australian Economic Papers, Wiley Blackwell, vol. 52(2), pages 79-96, June.
    3. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521710091.
    4. Labys, W C & Lesourd, J B & Badillo, D, 1998. "The existence of metal price cycles," Resources Policy, Elsevier, vol. 24(3), pages 147-155, September.
    5. Bergmeir, Christoph & Benítez, José M., 2012. "Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(i07).
    6. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    7. Ma, Weimin & Zhu, Xiaoxi & Wang, Miaomiao, 2013. "Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm," Resources Policy, Elsevier, vol. 38(4), pages 613-620.
    8. Cortazar, Gonzalo & Eterovic, Francisco, 2010. "Can oil prices help estimate commodity futures prices? The cases of copper and silver," Resources Policy, Elsevier, vol. 35(4), pages 283-291, December.
    9. Roberts, Mark C., 2009. "Duration and characteristics of metal price cycles," Resources Policy, Elsevier, vol. 34(3), pages 87-102, September.
    10. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521883818.
    11. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    12. Ahmed A. A. Khalifa & Hong Miao & Sanjay Ramchander, 2011. "Return distributions and volatility forecasting in metal futures markets: Evidence from gold, silver, and copper," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(1), pages 55-80, January.
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    More about this item

    Keywords

    Neural networks; Autoregressive integrated moving average (ARIMA); Time series analysis; Copper; Price forecasting; New York Commodity Exchange (COMEX);
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices

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