2091 pipeline on accessible platforms (Excel + @SIMULATOR 5.0), which is in accordance with practices described in studies on simulation applied to mining and decision making. This framework allows mining companies to: a. Quantifying uncertainty in early stages b. Prioritize critical variables using variance contribution analysis c. Replace static scenarios with stochastic modeling d. Basing investment, optimization, or mitigation decisions on probabilistic metrics 6. METHODOLOGY Monte Carlo Simulation (MCS) applied to the financial risk analysis of gold mining projects. This method is based on the following: 1. Probabilistic modeling of the project's critical variables a. We assign probability distributions to the key variables: b. Price of Gold c. Metallurgical recovery d. Mining costs e. Processing costs f. The use of empirical/theoretical distributions (Normal, Lognormal, Triangular) is consistent with the literature on mining and financial modeling. 2. Incorporation of correlations between variables. 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.
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