IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v70y2021ics0301420720309880.html
   My bibliography  Save this article

Estimating copper concentrates benchmark prices under dynamic market conditions

Author

Listed:
  • Díaz-Borrego, Francisco J.
  • Escobar-Peréz, Bernabé
  • Miras-Rodríguez, María del Mar

Abstract

Copper concentrates are the primary product sold by copper mines to traders or smelters. Their price is set in private agreements following unofficial market practices which, along with the fact of the wide variety of concentrates layouts traded worldwide, make problematic for any market participant to have a clear price reference for a specific concentrate. This paper presents a copper concentrates benchmark price model which provides suitable short-term price estimations based on metals and discounts forecasts, as well as on copper, gold and silver spot and future prices data from the LME and COMEX. The model, which redeems considerably low forecast error values for the short-term concentrate prices, constitutes a useful and applicable tool for miners, traders and smelters to set a benchmark price level for their copper concentrate transactions, also helping them optimize their operations, as well as estimate their immediate liquidity needs or their actual necessity to hedge the price risks associated to their concentrate trading. In addition, different concentrates layouts have been analyzed to test the model's behavior with the most common specification and blends of copper concentrates demanded by the market, as well as to portray the model's forecasting capacity and its ability to convey information on the future relevance of the different components of pricing in the most frequent timeframe in which copper concentrate trading takes place.

Suggested Citation

  • Díaz-Borrego, Francisco J. & Escobar-Peréz, Bernabé & Miras-Rodríguez, María del Mar, 2021. "Estimating copper concentrates benchmark prices under dynamic market conditions," Resources Policy, Elsevier, vol. 70(C).
    • Handle: RePEc:eee:jrpoli:v:70:y:2021:i:c:s0301420720309880
    DOI: 10.1016/j.resourpol.2020.101959
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420720309880
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2020.101959?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
    2. Dehghani, Hesam & Bogdanovic, Dejan, 2018. "Copper price estimation using bat algorithm," Resources Policy, Elsevier, vol. 55(C), pages 55-61.
    3. 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.
    4. Guj, Pietro & Chandra, Atul, 2019. "Comparing different real option valuation approaches as applied to a copper mine," Resources Policy, Elsevier, vol. 61(C), pages 180-189.
    5. 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.
    6. Martin Barlow & Yuri Gusev & Manpo Lai, 2004. "Calibration Of Multifactor Models In Electricity Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 101-120.
    7. Brennan, Michael J & Schwartz, Eduardo S, 1985. "Evaluating Natural Resource Investments," The Journal of Business, University of Chicago Press, vol. 58(2), pages 135-157, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nabavi, Zohre & Mirzehi, Mohammad & Dehghani, Hesam, 2024. "Reliable novel hybrid extreme gradient boosting for forecasting copper prices using meta-heuristic algorithms: A thirty-year analysis," Resources Policy, Elsevier, vol. 90(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
    2. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
    3. Reus, Lorenzo & Pagnoncelli, Bernardo & Armstrong, Margaret, 2019. "Better management of production incidents in mining using multistage stochastic optimization," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    4. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2020. "A random walk through the trees: Forecasting copper prices using decision learning methods," Resources Policy, Elsevier, vol. 69(C).
    5. Liu, Kailei & Cheng, Jinhua & Yi, Jiahui, 2022. "Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform," Resources Policy, Elsevier, vol. 75(C).
    6. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    7. Shen, Junjie & Huang, Shupei, 2022. "Copper cross-market volatility transition based on a coupled hidden Markov model and the complex network method," Resources Policy, Elsevier, vol. 75(C).
    8. Zhang, Hong & Nguyen, Hoang & Vu, Diep-Anh & Bui, Xuan-Nam & Pradhan, Biswajeet, 2021. "Forecasting monthly copper price: A comparative study of various machine learning-based methods," Resources Policy, Elsevier, vol. 73(C).
    9. Luo, Hongyuan & Wang, Deyun & Cheng, Jinhua & Wu, Qiaosheng, 2022. "Multi-step-ahead copper price forecasting using a two-phase architecture based on an improved LSTM with novel input strategy and error correction," Resources Policy, Elsevier, vol. 79(C).
    10. Cifuentes, Sebastián & Cortazar, Gonzalo & Ortega, Hector & Schwartz, Eduardo S., 2020. "Expected prices, futures prices and time-varying risk premiums: The case of copper," Resources Policy, Elsevier, vol. 69(C).
    11. Khoshalan, Hasel Amini & Shakeri, Jamshid & Najmoddini, Iraj & Asadizadeh, Mostafa, 2021. "Forecasting copper price by application of robust artificial intelligence techniques," Resources Policy, Elsevier, vol. 73(C).
    12. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    13. Li, Ning & Li, Jiaojiao & Wang, Qizhou & Yan, Dairong & Wang, Liguan & Jia, Mingtao, 2024. "A novel copper price forecasting ensemble method using adversarial interpretive structural model and sparrow search algorithm," Resources Policy, Elsevier, vol. 91(C).
    14. Nguyen, Duc Binh Benno & Prokopczuk, Marcel, 2019. "Jumps in commodity markets," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 55-70.
    15. Abdullah Almansour & Margaret Insley, 2016. "The Impact of Stochastic Extraction Cost on the Value of an Exhaustible Resource: An Application to the Alberta Oil Sands," The Energy Journal, , vol. 37(2), pages 61-88, April.
    16. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
    17. Alain Bensoussan & Benoit Chevalier-Roignant & Alejandro Rivera, 2022. "A model for wind farm management with option interactions," Post-Print hal-04325553, HAL.
    18. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    19. Björn Lutz, 2010. "Pricing of Derivatives on Mean-Reverting Assets," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-02909-7, October.
    20. Luis M. Abadie & José M. Chamorro, 2009. "Monte Carlo valuation of natural gas investments," Review of Financial Economics, John Wiley & Sons, vol. 18(1), pages 10-22, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:70:y:2021:i:c:s0301420720309880. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.