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Evaluation of a mining project under the joint effect of commodity price and exchange rate uncertainties using real options valuation

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  • Md. Aminul Haque
  • Erkan Topal
  • Eric Lilford

Abstract

Cash flows generated by mining projects tend to be volatile and are extensively influenced by exogenous variables, notably commodity prices and exchange rates. The traditional discounted cash flow (DCF) method, which is normally used for economic feasibility studies and mining project evaluations, presents inconsistencies because the method fails to adequately address uncertainties and operational flexibilities and often ignores certain specific market conditions. Numerous studies have been carried out for mining project evaluations using the real options valuation (ROV) technique for assessing commodity price uncertainty, but there is no research on the combined effects of price and exchange rate uncertainties. Therefore, in order to assess the economic viability of a mining project more accurately, the commodity price and its inherent volatility, the exchange rate and its inherent volatility, and the correlation parameters between them have been incorporated into the model and used in the evaluation process. One of the interesting findings revealed in the study is that project values are overestimated if only commodity price uncertainty is considered in evaluating the project value instead of the joint effect of commodity price and exchange rate uncertainties. This new ROV technique will explore the opportunity to utilize an alternative methodology for approximating project values and to identify valuation opportunities to enhance economic gains or to mitigate economic losses, where the DCF valuation method does not.

Suggested Citation

  • Md. Aminul Haque & Erkan Topal & Eric Lilford, 2017. "Evaluation of a mining project under the joint effect of commodity price and exchange rate uncertainties using real options valuation," The Engineering Economist, Taylor & Francis Journals, vol. 62(3), pages 231-253, July.
  • Handle: RePEc:taf:uteexx:v:62:y:2017:i:3:p:231-253
    DOI: 10.1080/0013791X.2016.1217366
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    Cited by:

    1. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
    2. Linder, Eric & Marbuah, George, 2019. "The cost of transparency: Stock market reactions to introduction of the Extractive Sector Transparency Measures Act in Canada," Resources Policy, Elsevier, vol. 63(C), pages 1-1.

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