Monte Carlo Simulation

Monte Carlo simulation is a brute-force forecasting method: instead of using formulas, you run thousands of random scenarios and observe the distribution of outcomes. The process: (1) Estimate return distributions for each asset (mean, volatility, correlations); (2) Generate random draws from these distributions; (3) Simulate portfolio evolution over time (daily/monthly steps); (4) Repeat 10,000+ times; (5) Analyze the distribution of final wealth. For example, running 10,000 simulations of a 60/40 portfolio over 30 years shows the range of outcomes — median $2.5M, 10th percentile $1.2M, 90th percentile $4.8M. Monte Carlo captures non-linearity, path dependency, and rebalancing effects that formulas miss. Used for retirement planning, VaR estimation, and stress testing.