Unlike many traditional approaches, this paper integrates correlation matrices to represent real dependencies between factors (e.g., price–cost or grade–recovery), which is recommended in modern risk analysis and Monte Carlo simulations. This aligns the model with advanced practices for avoiding bias in the NPV variance. 3. Execution of thousands of Monte Carlo iterations The model runs thousands of simulations to generate a complete Net Present Value (NPV) distribution, following methodologies recently applied to gold mining projects and mining economic analysis. 4. Derivation of advanced risk metrics From the NPV distribution, we obtain: • Probability of a positive NPV • Cumulative curves of economic value • Value at Risk (VaR) of the project, a financial risk metric widely adopted in finance and recently applied to the evaluation of mining projects. 5. Analysis of contribution to variance The model calculates how each variable explains the total variance in NPV, allowing for the identification of critical risk drivers and guiding mitigation strategies. Mining literature recommends this approach as a complement to simulation when seeking robust decision-making. CASE STUDY A Peruvian mining company is in the process of growth and is evaluating a project in the neighboring country to the south. The board of directors requests a risk analysis based on reserves, mining plan, operating costs, metallurgical recovery, and the price of gold and silver. The first step to begin the analysis is to identify the uncertainty variables and associate them with the type of distribution of the variable involved. Table 1 – Variables and type of distribution Uncertainty Variables Type of distribution Price of Gold Log normal
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