Monte Carlo retirement calculator
Guide · By Kevin Lan · Updated October 2026
A Monte Carlo retirement calculator runs your plan across thousands of possible market paths and reports the probability your money lasts: a chance of success, not a single straight-line guess.
What Monte Carlo adds
A Monte Carlo simulation runs your retirement plan across thousands of randomized market sequences and counts how often the money survives. The result is a success rate, say 90%, instead of one straight line. A plain projection assumes a single steady return every year; real markets deliver good and bad years in an order you can't predict, and that order matters.
Why the order of returns matters
The same average return can end very differently depending on when the bad years land. A steep loss early in retirement, while you're also withdrawing, can permanently shrink the portfolio. This is sequence-of-returns risk. A fixed-return calculator can't see it; Monte Carlo does, because it tries thousands of orderings and reports how many leave you solvent.
How a Monte Carlo simulation works
Under the hood, the method is a loop that trades one guaranteed answer for a distribution of them:
- Start with your plan: the portfolio, your withdrawal, and the number of years it must last.
- Draw a random return for the first year from a range calibrated to historical market behavior (an average and a volatility), instead of assuming the same return every year.
- Apply that return, then subtract that year's withdrawal, adjusted for inflation.
- Repeat year by year to the end of the horizon. That single sequence is one possible retirement, called a path.
- Run hundreds or thousands of paths, each with a different random order of good and bad years.
- The success rate is the share of paths in which the money never runs out.
How to read the result
A success rate of roughly 85–95% is commonly treated as comfortable. A perfect 100% often means you're over-saving and could retire sooner or spend more; well below about 75% suggests trimming spending, saving more, or working a little longer. It's a probability from historical-style assumptions: a stress test, not a promise.
Monte Carlo vs backtesting vs a fixed-return projection
Three methods answer the “will my money last” question in different ways:
| Method | How it models returns | What it tells you |
|---|---|---|
| Fixed-return projection | The same average return every year | A single straight-line estimate of your balance |
| Historical backtesting | Actual past return sequences, in order (the method behind the 4% rule) | Whether your plan survived every historical period |
| Monte Carlo | Thousands of randomized sequences from a calibrated distribution | The share of outcomes in which your money lasts |
Each is a stress test of a different kind. Backtesting asks whether you would have survived the past; Monte Carlo asks how you fare across many plausible futures. Neither is a promise, and using the 4% rule to size a target, then checking it with a simulation, is stronger than relying on either alone.
What moves your success rate
A handful of inputs drive the number, and knowing which helps you act on a weak result:
- Withdrawal rate. The biggest single dial: a lower rate raises success sharply.
- Spending flexibility. A willingness to trim in bad years is, in practice, the most powerful real-world lever, far more than squeezing out the last percent of return.
- Time horizon. A 45-year retirement has more chances to hit a bad sequence than a 30-year one, so the same plan scores lower the earlier you retire.
- Asset allocation. Too aggressive adds volatility; too conservative can fail to outpace inflation over a long horizon. Both extremes lower success for different reasons.
- Later income. Social Security or a pension starting mid-retirement raises the odds, because the portfolio carries fewer of the later years alone.
Why the odds can look more cautious than your projection
A fixed-return projection compounds at the average return, but the typical simulated path grows more slowly than the average. When returns swing from year to year, losses cost more than equal gains add back: a 20% gain followed by a 20% loss leaves you 4% down, not even. This gap is called volatility drag. With a 7% average real return and 15% annual volatility, the median path in a log-normal simulation compounds at about 6% a year, roughly one point below the average.
That is why a Monte Carlo result can read a little more cautious than a countdown built on the same 7%. Neither is wrong: the projection shows where the average return takes you, and the simulation shows how often the swings still leave you solvent. The higher the volatility you assume, the wider the gap; with volatility set to zero the two agree exactly.
Run it on your own plan
The calculator below finds your FIRE number and the year you reach it for free. The iPhone app adds the Monte Carlo chance-of-success simulation, which runs your plan across 2,000 market paths and tests the FIRE type you count down to (Lean, Barista, and Coast included), retiring on your actual FIRE date, and states the typical simulated growth rate next to the return you entered so the volatility drag is visible on your own numbers. It is part of Pro.
References
- Kitces M. Understanding sequence of return risk: safe withdrawal rates, bear-market crashes, and bad decades of returns. Nerd’s Eye View. kitces.com
- Pfau WD. Retirement Planning Guidebook. Vienna, VA: Retirement Researcher Media; 2021. Research on safe withdrawal rates and sequence-of-returns risk.
- Benz C, Rekenthaler J, Arnott A. The State of Retirement Income. Morningstar (updated annually). Analysis of safe starting withdrawal rates under current market conditions. morningstar.com
- Bengen WP. Determining withdrawal rates using historical data. Journal of Financial Planning. 1994;7(4):171–180. The paper that introduced the 4% withdrawal guideline.
- Cooley PL, Hubbard CM, Walz DT. Retirement savings: choosing a withdrawal rate that is sustainable. AAII Journal. 1998;20(2):16–21. Widely known as the “Trinity study.”
FIRE calculator
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Frequently asked questions
What is a Monte Carlo retirement simulation?
It's a method that runs your plan across hundreds or thousands of randomized market return sequences and reports the share in which your money lasts the whole retirement, a probability of success rather than a single projection.
What is a good Monte Carlo success rate?
Around 85–95% is widely considered comfortable. A 100% result may mean you're over-saving; below roughly 75% suggests lowering spending, saving more, or delaying retirement.
How does a Monte Carlo retirement simulation work?
It runs your plan through many randomized market sequences. Each year it draws a return from a range based on historical averages and volatility, applies your inflation-adjusted withdrawal, and repeats to the end of the horizon to form one path. After hundreds or thousands of paths, the success rate is the share in which the money lasts.
How accurate is a Monte Carlo retirement calculator?
It's only as good as its assumptions: the expected return, the volatility, and the inflation and withdrawal figures you give it. It doesn't predict the future; it maps a range of outcomes so you can see how much margin a plan has. Treat the success rate as a gauge for comparing plans, not a precise probability.
Why is my Monte Carlo success rate lower than my projection suggests?
Because of volatility drag. A straight-line projection compounds at the average return every year, while returns that swing grow more slowly along the typical path: at a 7% average with 15% volatility, the median simulated path grows about 6% a year. The simulation also counts the paths where bad years land early. Both effects make the odds read more cautious than a single-return projection on the same inputs.
Is Monte Carlo better than the 4% rule?
They answer different questions. The 4% rule is a quick benchmark for how much you need; Monte Carlo stress-tests a specific plan against many market paths. Using both, the rule to size the target and a simulation to check it, is stronger than either alone.
