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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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    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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