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Cash flow at risk valuation of mining project using Monte Carlo simulations with stochastic processes calibrated on historical data

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  • Mathieu Sauvageau
  • Mustafa Kumral

Abstract

Mining projects are subject to multiple sources of market uncertainties such as metal price, exchange rates, and their volatilities. Assessing a mining project's exposure to market risk usually requires Monte Carlo simulations to capture a range of probable outcomes. The probability of a major loss is extracted from the probability density function of simulated prices at a given time into the future. This article proposes an approach to calibrate the stochastic process to be used in Monte Carlo simulations. The simulations are then used for measuring the cash flow at risk of a mining project. To assess the performance of the proposed approach, a case study is conducted on a mining project. The results show that the calibration approach is robust and apt at fitting various stochastic processes to historical observations.

Suggested Citation

  • Mathieu Sauvageau & Mustafa Kumral, 2018. "Cash flow at risk valuation of mining project using Monte Carlo simulations with stochastic processes calibrated on historical data," The Engineering Economist, Taylor & Francis Journals, vol. 63(3), pages 171-187, July.
    • Handle: RePEc:taf:uteexx:v:63:y:2018:i:3:p:171-187
    DOI: 10.1080/0013791X.2017.1413150
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    Cited by:

    1. Kamel, Ahmed & Elwageeh, Mohamed & Bonduà, Stefano & Elkarmoty, Mohamed, 2023. "Evaluation of mining projects subjected to economic uncertainties using the Monte Carlo simulation and the binomial tree method: Case study in a phosphate mine in Egypt," Resources Policy, Elsevier, vol. 80(C).
    2. Ardian, Aldin & Kumral, Mustafa, 2020. "Incorporating stochastic correlations into mining project evaluation using the Jacobi process," Resources Policy, Elsevier, vol. 65(C).
    3. Aldin Ardian & Mustafa Kumral, 2021. "Enhancing mine risk assessment through more accurate reproduction of correlations and interactions between uncertain variables," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 34(3), pages 411-425, October.
    4. Achille N. Njike & Mustafa Kumral, 2019. "Mining corporate portfolio optimization model with company’s operational performance level and international risk," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 32(3), pages 307-315, November.

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