Monte Carlo Retirement Calculator
Estimate the probability that retirement savings will fund spending through life expectancy. The simulator runs thousands of reproducible return paths and reports retirement balance, ending-balance percentiles, and depletion risk in today’s dollars.
| Age | 10th percentile | Median | 90th percentile | Plans still funded |
|---|
Percentiles describe simulated paths, not guaranteed outcomes. “Still funded” means no required retirement withdrawal was missed by that age.
What a Monte Carlo retirement simulation does
A deterministic retirement calculator applies the same return every year. A Monte Carlo calculator instead generates many possible sequences of gains and losses. This exposes sequence-of-returns risk: poor markets near retirement can cause failure even when the long-run average return appears adequate.
This page consolidates Monte Carlo retirement calculator, retirement simulation, retirement probability of success, investment Monte Carlo, and retirement savings simulator intent. It does not price options or model casino games.
Model and formulas
Accumulation: Bt+1 = Bt(1 + Rt) + 12C
Retirement: Bt+1 = max(Bt − (S − I), 0)(1 + Rt)
Lognormal return: Rt = exp(μlog + σlogZt) − 1
C is the monthly contribution, S is annual spending, I is annual pension or Social Security income, and Z is a standard normal draw. The lognormal parameters are converted from the entered arithmetic expected return and volatility so simulated returns cannot fall below −100%. Contributions are added at each year-end; net retirement spending is withdrawn at each year-start.
Verified zero-volatility example
At age 60, start with $100,000, contribute $1,000 monthly until retiring at 62, plan through age 65, spend $30,000 per year, and receive $10,000 per year. With 0% return and 0% volatility, savings at retirement are $124,000. Three net withdrawals of $20,000 leave $64,000, so every simulated path succeeds. The calculator reproduces 100% success, $124,000 at retirement, and $64,000 at the end.
How to use the calculator
- Enter current age, retirement age, and the last age the plan must fund.
- Enter savings and monthly contributions in today’s purchasing power.
- Enter annual retirement spending and reliable annual income in the same units.
- Use an expected real return after inflation and a matching annual volatility.
- Run 1,000–10,000 simulations. Keep the seed unchanged when comparing scenarios.
Limits and assumptions
This is a planning model, not a forecast. A high success percentage does not guarantee retirement security, and a low percentage does not mean failure is certain.
- Annual returns are independent and lognormally distributed with constant expected return and volatility.
- All amounts are real, so inflation is already removed. Do not enter nominal returns with real spending.
- Taxes, account rules, required minimum distributions, fees, asset allocation changes, and one-time expenses are excluded.
- Pension or Social Security above spending is added to the portfolio at the start of each retirement year.
- Success means every modeled retirement withdrawal was fully funded through the selected final age.
- Values are limited to 1 quadrillion; expected real return is limited to −20% to 20%, volatility to 0%–100%, and ages to 18–120.
Frequently asked questions
What is a good Monte Carlo retirement success rate?
There is no universal threshold. Many planners examine scenarios near 80%–95%, but the appropriate margin depends on spending flexibility, guaranteed income, longevity risk, and the consequences of shortfall. Review the actual assumptions instead of treating one percentage as a pass/fail grade.
Why use a fixed seed?
A seed makes the random paths reproducible. When two plans use the same seed, changes in results come from the assumptions rather than a different random sample.
Why are returns lognormal?
A lognormal wealth model prevents a one-year return below −100%. It remains a simplification: real markets can show changing volatility, serial correlation, and unusually frequent extreme events.
Is this different from a standard retirement calculator?
Yes. A standard calculator solves one fixed-return path. Monte Carlo analysis evaluates thousands of return sequences and reports a distribution and probability of funding the plan.