Monte Carlo simulation runs thousands of randomized market scenarios to show you not just one possible retirement outcome, but the full range — from best case to worst case. Instead of asking "will my money last?", it answers "what is the probability my money will last?"
Monte Carlo simulation is the backbone of modern retirement planning. Rather than assuming a single fixed rate of return — say, 7% per year — a Monte Carlo simulation runs thousands of randomized scenarios, each with a unique sequence of market returns drawn from a probability distribution. The result is not a single number but a range of possible outcomes, giving you a realistic picture of how your retirement plan might perform under different market conditions.
How It Works
- Define inputs: starting portfolio balance, asset allocation, expected returns, volatility, withdrawal strategy, and time horizon.
- Generate random returns: for each simulated year, draw random returns for each asset class from a specified distribution (normal, Student-t, or skewed Student-t).
- Apply withdrawals and income: subtract expenses, add income streams (Social Security, pensions), and rebalance the portfolio.
- Repeat: run the simulation thousands of times — Retirement Lab runs 10,000 iterations (free tier) or 50,000 iterations (pro tier).
- Aggregate results: calculate percentile bands (10th, 25th, 50th, 75th, 90th) and the overall success rate.
Each iteration represents one possible "future" for your portfolio. No single iteration is a prediction — the power lies in the distribution of outcomes.
Why It Matters for Retirement Planning
Traditional retirement calculators use a fixed average return, which hides the enormous impact of volatility and sequence of returns. A Monte Carlo simulation reveals:
- Success rate: the percentage of scenarios where your money lasts through retirement
- Tail risk: what happens in the worst 10% of outcomes
- The impact of spending changes: how adjusting your withdrawal rate shifts the entire distribution
For example, two retirees with identical average returns of 7% could have vastly different outcomes depending on when the good and bad years occur. Monte Carlo captures this path dependency.
A Practical Example
Consider a retiree with a $1,000,000 portfolio, a 4% initial withdrawal rate ($40,000/year), and a 30-year horizon. A simple spreadsheet projection at 7% annual return shows the portfolio growing to over $2 million. But a Monte Carlo simulation with realistic volatility (15% standard deviation) might reveal:
- 90th percentile: $3.2 million remaining
- 50th percentile (median): $1.1 million remaining
- 10th percentile: portfolio depleted by year 24
- Success rate: 82%
That 82% success rate — invisible in a fixed-return projection — is critical information for planning.
Interactive chart: monte-carlo-fan
Portfolio value percentile bands (10th–90th) over a 30-year retirement
Coming soon
Beyond the Normal Distribution
Most basic Monte Carlo simulators assume returns follow a normal (Gaussian) distribution. In reality, financial markets exhibit fat tails — extreme events occur far more often than a bell curve predicts. Retirement Lab uses the Student's t-distribution with Fernandez-Steel skewness to model these real-world characteristics, producing more realistic worst-case scenarios.
Frequently Asked Questions
- How many Monte Carlo iterations are enough for retirement planning?
- Most financial planners recommend at least 10,000 iterations for stable percentile estimates. Retirement Lab runs 10,000 (free) or 50,000 (pro) iterations. Beyond 50,000 the percentile bands converge and additional iterations yield diminishing returns.
- Is Monte Carlo simulation better than a spreadsheet for retirement planning?
- Yes. Spreadsheets use a single fixed return (e.g., 7%/year) and hide the impact of volatility and sequence-of-returns risk. Monte Carlo reveals the full range of outcomes — including worst-case scenarios — and gives you a probability of success rather than a single number.
- What is a good Monte Carlo success rate for retirement?
- Most financial planners target an 80–90% success rate. A 100% rate usually means you are spending too conservatively. Below 75% suggests meaningful risk of running out of money. The right target depends on your flexibility to cut spending if needed.