Monte Carlo Simulation: Thousands of Retirement Scenarios

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

1. Define inputs: starting portfolio balance, asset allocation, expected returns, volatility, withdrawal strategy, and time horizon.
2. Generate random returns: for each simulated year, draw random returns for each asset class from a specified distribution (normal or Student-t, optionally skewed).
3. Apply withdrawals and income: subtract expenses, add income streams (Social Security, pensions), and rebalance the portfolio.
4. Repeat: run the simulation thousands of times - Retirement Lab runs 1,000 iterations (free plan) or 50,000 iterations (Pro).
5. 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. For a deeper walkthrough, see our guide on how Monte Carlo retirement calculators work.

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 with a steady return shows the portfolio lasting comfortably. A Monte Carlo simulation of the same plan - 60/40 stocks and bonds, spending raised with inflation - tells a fuller story. In Retirement Lab's engine (Balanced outlook, 50,000 simulated futures):

• Success rate: about 83% - in roughly 17 of every 100 futures, the money runs out before year 30
• Typical outcome: about 826,000 left at the end, in today's money

That 17% chance of running short - invisible in a fixed-return projection - is critical information for planning.

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. We cover why this matters in fat tails and retirement planning. Retirement Lab uses the Student's t-distribution with Fernandez-Steel skewness to model these real-world characteristics, producing more realistic worst-case scenarios.

How Retirement Lab Addresses This

Retirement Lab runs up to 50,000 Monte Carlo iterations per simulation, with fat-tail distributions, correlated 3-asset returns, and four configurable spending strategies. The free tier includes 1,000 iterations with normal distributions; Pro unlocks the full stress-testing toolkit.