Monte Carlo Simulation: Understanding Your Portfolio's Range of Futures
How 1,000 simulated paths reveal what stress testing cannot: the probability distribution of your long-term outcomes.
Stress testing tells you what your portfolio would have lost in 2008. Monte Carlo simulation tells you something different and complementary: across all possible futures consistent with your portfolio's characteristics, what is the probability you achieve your financial goal? These two questions require different tools, different mathematics, and produce different — equally essential — insights.
The difference between stress testing and Monte Carlo
Stress testing is deterministic and backward-looking: it applies a specific historical scenario to your portfolio and produces a single outcome. It answers: "What would have happened to me in 2008?" It is powerful because the scenarios are real — the correlations, the liquidity breakdown, the panic behaviour all happened and are modelled with historical fidelity.
Monte Carlo simulation is probabilistic and forward-looking: it generates thousands of possible future paths by sampling from a statistical model of returns, volatilities, and correlations. It answers: "Across the full distribution of plausible futures, what is the range of outcomes I should expect?" A single run produces one number. Monte Carlo produces a distribution — and it is the shape of that distribution that matters.
Full article unlocked for subscribers
Join Architect for $59/mo and get full access to all deep dive articles, the Sunday Deep Dive newsletter, and the complete stress-test toolkit.
Not financial advice. Educational content only.