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Copper price estimation using bat algorithm

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  • Dehghani, Hesam
  • Bogdanovic, Dejan

Abstract

The most effective parameter on the value of mining projects is metal price volatility. Therefore, knowing the metal price volatility can help the managers and shareholders of the mining projects to make the right decisions for extending or restricting the mining activities. Nowadays, classical estimation methods cannot correctly estimate the metal prices volatility due to their frequent variations in the past years. For solving this problem, it is necessary to use the artificial algorithms that have a good ability to predict the volatility of the various phenomena. In this paper, the Bat algorithm was used to predict the copper price volatility. Accordingly, Brownian motion with mean reversion (BMMR) was chosen as the most suitable time series function with the root mean square error (RMSE) of 0.449. Then, the estimation parameters of the equation were modified using Bat algorithm. Finally, it is concluded that the determined equation with 0.132 of RMSE can predict the copper price better than the classic estimation methods.

Suggested Citation

  • Dehghani, Hesam & Bogdanovic, Dejan, 2018. "Copper price estimation using bat algorithm," Resources Policy, Elsevier, vol. 55(C), pages 55-61.
    • Handle: RePEc:eee:jrpoli:v:55:y:2018:i:c:p:55-61
    DOI: 10.1016/j.resourpol.2017.10.015
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    References listed on IDEAS

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    1. 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.
    2. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    3. Liu, Chang & Hu, Zhenhua & Li, Yan & Liu, Shaojun, 2017. "Forecasting copper prices by decision tree learning," Resources Policy, Elsevier, vol. 52(C), pages 427-434.
    4. Morales, Lucía & Andreosso-O'Callaghan, Bernadette, 2011. "Comparative analysis on the effects of the Asian and global financial crises on precious metal markets," Research in International Business and Finance, Elsevier, vol. 25(2), pages 203-227, June.
    5. Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
    6. Chen, Yanhui & He, Kaijian & Zhang, Chuan, 2016. "A novel grey wave forecasting method for predicting metal prices," Resources Policy, Elsevier, vol. 49(C), pages 323-331.
    7. Dehghani, Hesam & Ataee-pour, Majid & Esfahanipour, Akbar, 2014. "Evaluation of the mining projects under economic uncertainties using multidimensional binomial tree," Resources Policy, Elsevier, vol. 39(C), pages 124-133.
    8. 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.
    9. Dehghani, Hesam & Ataee-pour, Majid, 2012. "Determination of the effect of operating cost uncertainty on mining project evaluation," Resources Policy, Elsevier, vol. 37(1), pages 109-117.
    10. Shafiee, Shahriar & Topal, Erkan, 2010. "An overview of global gold market and gold price forecasting," Resources Policy, Elsevier, vol. 35(3), pages 178-189, September.
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