3. Introduction of Financial Value at Risk (VaR) Applied to Gold Mining Projects. - Financial literature establishes VaR as a standard metric for measuring tail risk and extreme losses. The contribution of this paper is to bring this metric widely used in banking and portfolio management to the field of mining project evaluation, allowing for the estimation of: a. Risk of extreme NPV loss b. Probability of not achieving the target return c. Downside range under uncertainty d. This methodological shift strengthens the governance of decisions in capitalintensive investments. 4. A replicable framework to support data-driven corporate decision making. Aligned with contemporary trends towards smart mining, the paper proposes a reproducible analytical 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 metrica 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.
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