Abstract
<jats:p>he 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.</jats:p>