Commodities underpin global trade, fueling economies as essential inputs for industries ranging from energy systems and agriculture to semiconductors and renewable technologies. Price volatility poses acute risks for commodity-dependent economies-nations deriving over 60% of export revenue from a single resource-exacerbating fiscal instability and socio-political fragility. This thesis examines how shocks in oil, copper, and lithium prices-driven by their roles in AI, electric vehicles, and energy transitions-impact economic growth in vulnerable, export-concentrated countries. To project future dynamics, Monte Carlo simulations are integrated with stochastic differential equation (SDE) models-combining geometric Brownian motion and mean-reverting jump diffusion-to analyze historical price trends and simulate probabilistic forecasts under geopolitical disruptions, technological shifts, and climate-related supply constraints. Results reveal asymmetric economic impacts, with resource-dependent nations disproportionately exposed to risks from AI-driven demand surges and decarbonization pressures. The study underscores the urgency of diversification, stabilization funds, and adaptive policies to mitigate volatility. By linking commodity-specific microdynamics with macroeconomic trends, the research demonstrates how resource wealth can transition from a vulnerability to a sustainable growth catalyst. Methodologically, it advances frameworks that leverage Monte Carlo techniques to quantify non-linear market behaviors and stochastic uncertainties, offering policymakers tools to simulate price trajectories and manage systemic risks. Findings advocate for scenario-based modeling to enhance resilience amid escalating volatility, balancing global industrial demands with equitable development. Ultimately, this work equips stakeholders to harness data-driven insights and forward-looking simulations, transforming commodity dependencies into opportunities for socio-economic stability.
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