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Long-term iron ore price modeling: Marginal costs vs. incentive price

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  • Pustov, Alexander
  • Malanichev, Alexander
  • Khobotilov, Ilya

Abstract

The paper studies and applies the approaches to forecast long-term (LT) real prices of iron ore. This price is crucial for valuation of investments in Greenfield iron ore projects on the horizon of more than 5 years. The forecast is obtained by three different approaches which are usually used by investment bank analysts: marginal costs approach and 2 approaches based on calculation of incentive price. The paper concludes that there has been a structural shift on the iron ore market and LT iron ore prices will be higher by 20–30% than the average of industry forecasters suggest. This is related to the 2 key factors which were taken into account in this study—depletion of existing iron ore deposits and targeted return on investments for new projects. In addition, escalated industry costs inflation is claimed to be the factor which will bolster nominal iron ore prices at high levels in the long-term. Using a Monte-Carlo simulation approach, confidence interval for future iron ore price was estimated.

Suggested Citation

  • Pustov, Alexander & Malanichev, Alexander & Khobotilov, Ilya, 2013. "Long-term iron ore price modeling: Marginal costs vs. incentive price," Resources Policy, Elsevier, vol. 38(4), pages 558-567.
    • Handle: RePEc:eee:jrpoli:v:38:y:2013:i:4:p:558-567
    DOI: 10.1016/j.resourpol.2013.09.003
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    References listed on IDEAS

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    Cited by:

    1. Su, Chi-Wei & Wang, Kai-Hua & Chang, Hsu-Ling & Dumitrescu–Peculea, Adelina, 2017. "Do iron ore price bubbles occur?," Resources Policy, Elsevier, vol. 53(C), pages 340-346.
    2. Liu, Yanxin & Li, Huajiao & Guan, Jianhe & Liu, Xueyong & Guan, Qing & Sun, Qingru, 2019. "Influence of different factors on prices of upstream, middle and downstream products in China's whole steel industry chain: Based on Adaptive Neural Fuzzy Inference System," Resources Policy, Elsevier, vol. 60(C), pages 134-142.
    3. Bazhanov, Andrei, 2018. "Difficulties in the forecasting of iron ore price: a review," MPRA Paper 87881, University Library of Munich, Germany, revised 12 Jul 2018.
    4. Juan Ignacio Guzmán & Enrique Silva, 2018. "Copper price determination: fundamentals versus non-fundamentals," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 31(3), pages 283-300, October.
    5. Chen, Wenhui & Lei, Yalin & Jiang, Yong, 2016. "Influencing factors analysis of China’s iron import price: Based on quantile regression model," Resources Policy, Elsevier, vol. 48(C), pages 68-76.
    6. Kim, Yoochan & Ghosh, Apurna & Topal, Erkan & Chang, Ping, 2023. "Performance of different models in iron ore price prediction during the time of commodity price spike," Resources Policy, Elsevier, vol. 80(C).
    7. Yufeng CHEN & Shuo YANG, 2022. "How Does the Reform in Pricing Mechanism Affect the World’s Iron Ore Price: A Time-Varying Parameter SVAR Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 83-103, April.
    8. Yoochan Kim & Apurna Ghosh & Erkan Topal & Ping Chang, 2022. "Relationship of iron ore price with other major commodity prices," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(2), pages 295-307, June.
    9. 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).

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    More about this item

    Keywords

    Marginal costs; Incentive price; Long-term price; Iron ore; Forecasting;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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