Financial modeling of mining projects at the feasibility study stage with account of stochastic dynamics of the mineral commodity prices

DOI: https://doi.org/10.30686/1609-9192-2026-2-120-128

Читать на русскоя языке Y.A. Limareva, I.V. Zaychikova, N.Yu. Barkova, O.A. Borodina
Financial University under the Government of the Russian Federation, Moscow, Russian Federation
Russian Mining Industry №2/ 2026 p. 120-128

Abstract: The article discusses the development and testing of a comprehensive methodology for financial modeling of mining projects at the feasibility study stage, integrating stochastic modeling of the mineral commodity price dynamics and the Monte Carlo simulation. The relevance of the study is determined by the unprecedented volatility of the global commodity markets in 2023–2025: the gold price rose from $2,600/oz to a record high of $4,379/oz, the LME copper price reached $11,200/t with an annual increase of approximately 40%, creating a fundamentally new landscape of uncertainty for investment decisions. The aim of the study is to quantitatively assess the divergence between the deterministic and stochastic estimates of the key investment indicators for mining projects and to justify the methodological advantages of the probabilistic approach. The Geometric Brownian motion, the Ornstein–Uhlenbeck mean-reverting process, and the Schwartz–Smith two-factor model were applied as the stochastic price dynamics models with their parameters calibrated using the LME and COMEX futures contract data for the period of 2015–2025. The empirical base includes technical and economic parameters of three typical projects: a copper open-pit mine (CAPEX $1,420 million), an underground gold mine (CAPEX $980 million), and a polymetallic copper-gold deposit (CAPEX $1,850 million). The results of 10,000 Monte Carlo simulations demonstrate a systematic divergence between the deterministic and stochastic NPV in the range of −10,9 to −18,2%, with the NPV coefficients of variation of 0,31–0,58 depending on the project type and the price dynamics model. The probability of a negative NPV, which cannot be detected using the deterministic approach, varies from 6,2% to 12,7%. The Schwartz–Smith two-factor model demonstrates the lowest RMS calibration error against the futures data (RMSE = 3,8% for copper). The results justify the need to integrate the stochastic methods into the conventional feasibility study practice and allow quantifying the “cost of uncertainty” for the investor.

Keywords: financial modeling of mining projects, stochastic price dynamics, Monte Carlo simulation, feasibility study, Schwartz–Smith two-factor model, net present value, investment risk analysis

For citation: Limareva Y.A., Zaychikova I.V., Barkova N.Yu., Borodina O.A. Financial modeling of mining projects at the feasibility study stage with account of stochastic dynamics of the mineral commodity prices. Russian Mining Industry. 2026;(2):120–128. https://doi.org/10.30686/1609-9192-2026-2-120-128

Article info

Received: 17.01.2026

Revised: 19.02.2026

Accepted: 03.03.2026

Information about the authors

Yulia A. Limareva – Cand. Sci. (Ped.), Associate Professor of the Department of General and Project Management, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; https://orcid.org/0000-0001-7941-1459; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Inna V. Zaychikova – Cand. Sci. (Ped.), Associate Professor of the Department of Mathematics and Data Analysis, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Natalya Yu. Barkova – Cand. Sci. (Econ.), Associate Professor of the Department of General and Project Management, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; https://orcid.org/0000-0002-6583-8950; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Olga A. Borodina – Senior Lecturer, Department of General and Project Management, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; https://orcid.org/0000-0001-7151-4891; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

References

1. Zaernyuk V.M., Nesterenko Yu.N. Financial modeling in conditions of uncertainty in relation to mining enterprises. Ekonomika i Upravlenie: Problemy, Resheniya. 2025;2(8)):47–52. (In Russ.) https://doi.org/10.36871/ek.up.p.r.2025.08.02.004

2. Chupin A.L., Morkovkin D.E., Ekaterinovskaya M.A., Orusova O.V., Koroleva I.V. Uncertainty in assessing the economic efficiency of investment projects for coal mining enterprises. Ugol’. 2025;(2):75–82. (In Russ.) https://doi.org/10.18796/0041-5790-2025-2-75-82

3. Zaernyuk V.M. The use of simulation models to evaluate mining projects in conditions of depletion of mineral reserves. Financial Analytics: Science and Experience. 2025;18(3):17–26. (In Russ.) https://doi.org/10.24891/pyxbhp

4. Kaurova O.V., Zaernyuk V.M. Application of a probabilistic (stochastic) approach when choosing a metal price forecasting model. Vestnik of the Russian University of Cooperation. 2024;(2):24–29. (In Russ.)

5. Klyuev R.V. Modeling and optimization of energy intensity of basic flow processes at mines. Mining Informational and Analytical Bulletin. 2025;(4):170–181. (In Russ.) https://doi.org/10.25018/0236_1493_2025_4_0_170

6. Cardozo F.A.C., Petter C.O., Albuquerque N.R. Monte Carlo simulation risk analysis for underground mining projects. Tecnologia em Metalurgia, Materiais e Mineração. 2022;19:e2681. https://doi.org/10.4322/2176-1523.20222681

7. Gubanov R.S. Prospects of development of coal mining projects in the Arctic zone of the Russian Federation. Oil and Gas Technologies. 2024;(1):58–64. (In Russ.) https://doi.org/10.32935/1815-2600-2024-150-1-58-64

8. Borisov I.А., Kosorukov O.A., Mishchenko A.V., Tsurkov V.I. Optimization of enterprise production programs taken into account of uncertainty. Journal of Computer and System Sciences International. 2024;(4):77–92. (In Russ.) https://doi.org/10.31857/S0002338824040052

9. Borisov I.A., Kosorukov O.A., Mishchenko A.V., Tsurkov V.I. Models for assessing the effectiveness of the project of creating a production enterprise. Journal of Computer and System Sciences International. 2025;(2):70–81. (In Russ.) Available at: https://journals.rcsi.science/0002-3388/article/view/304277 (accessed: 27.12.2025).

10. Sokolov I.A., Suchkova O.V., Kazakova J.E., Repkina E.V. A study of budget vulnerability in commodity-dependent economies: A methodology for accounting for price shocks in exported resources. Terra Economicus. 2025;23(2):77–91. (In Russ.) https://doi.org/10.18522/2073-6606-2025-23-2-77-91

11. Dimitrakopoulos R.G., Abdel Sabour S.A. Evaluating mine plans under uncertainty: Can the real options make a difference? Resources Policy. 2007;32(3):116–125. https://doi.org/10.1016/j.resourpol.2007.06.003

12. Aksenov D.A., Toropov V.V., Mazurchuk T.M. Short-term forecasting and investment in russian oil and gas companies taking into account benchmark markers. Finance: Theory and Practice. 2025;29(5):164–177. https://doi.org/10.26794/2587-5671-2025-29-5-164-177

13. Karaev A.K., Borisova O.V. Prospective Models of Financial Forecasting of budget Revenues. Finance: Theory and Practice. 2025;29(1):20–33. https://doi.org/10.26794/2587-5671-2025-29-1-20-33

14. Trifonov P.V., Alferov V.N., Mokrova L.P., Bratarchuk T.V., Seryshev R.V. Assessment of technical and economic efficiency of the copper ore concentration processes based on the use of new flotation agents and optimization of the process parameters. Russian Mining Industry. 2024;(5S):98–104. (In Russ.) https://doi.org/10.30686/1609-9192-2024-5S-98-104

15. Savvateev E.V. Assessment of the effectiveness of carbon capture and storage (CCS) technologies in the Russian industrial sector. Voprosy Ecologii. 2024;37(1):245–274. (In Russ.) Available at: https://grreview.ru/index.php/wej/article/view/185 (accessed: 27.12.2025).

16. Abdel Sabour S.A., Dimitrakopoulos R. Incorporating geological and market uncertainties and operational flexibility into open pit mine design. Journal of Mining Science. 2011;47(2):191–201. https://doi.org/10.1134/S1062739147020067

17. Pindyck R.S. The long-run evolution of energy prices. The Energy Journal. 1999;20(2):1–27. Available at: https://web.mit.edu/rpindyck/www/Papers/Long-Run_Evolution.pdf (accessed: 27.12.2025).

18. Haque M.A., Topal E., Lilford E. A numerical study for a mining project using real options valuation under commodity price uncertainty. Resources Policy. 2014;39:115–123. https://doi.org/10.1016/j.resourpol.2013.12.004

19. Gligorić Z., Gligorić M., Halilović D., Beljić Č., Urošević K. Hybrid stochastic-grey model to forecast the behavior of metal price in the mining industry. Sustainability. 2020;12(16):6533. https://doi.org/10.3390/su12166533