Monte Carlo Retirement Calculator
A deterministic retirement projection assumes the same return every year, which masks the real risk of bad sequence of returns. Monte Carlo simulation addresses this by running 1,000 independent retirement scenarios, each with a different random sequence of annual returns drawn from your assumed return distribution. The survival probability is the percentage of simulations where your portfolio lasts the full retirement period. This gives a much richer picture of retirement security than a single projected number.
Monte Carlo simulation methodology
Each year: Return(y) = Normal(mean, std dev) using Box-Muller transform
Balance(y) = (Balance(y-1) - Withdrawal) * (1 + Return(y))
Survival = Balance(end) > 0
Survival Probability = Successful simulations / Total simulations * 100
1,000 independent simulations per run
The Box-Muller transform converts uniform random numbers into normally distributed return values. The withdrawal is taken at the beginning of each year (conservative assumption).
Interpreting your results
- A survival probability above 90% is generally considered strong; 80-90% is acceptable for most planners.
- The 10th percentile balance shows the worst realistic outcome (the bottom 10% of scenarios).
- The 90th percentile balance shows the best realistic outcome: many simulations will produce legacy wealth far beyond what you need.
- If survival probability is below 80%, consider: reducing withdrawal, delaying retirement, working part-time, or adjusting asset allocation.
- This simulation does not model Social Security, pension income, or dynamic spending adjustments. Adding guaranteed income sources improves actual success rates.
Monte Carlo retirement: frequently asked questions
What is a Monte Carlo simulation for retirement?
A Monte Carlo simulation runs hundreds or thousands of random scenarios using historical or assumed return distributions to test whether a portfolio survives a retirement. Instead of assuming a constant return each year, it uses random returns drawn from a distribution to model real market uncertainty.
What does the survival probability mean?
The survival probability (or success rate) is the percentage of simulated scenarios in which your portfolio had money remaining at the end of your retirement period. A 90% success rate means the portfolio survived in 90 out of 100 simulations. Most financial planners target 85-95%.
What return and volatility assumptions should I use?
The historical annualized real return on a 60/40 portfolio (stocks and bonds) has been approximately 5-6% with a standard deviation of about 11-12%. Using a lower expected return (4-5%) is more conservative and appropriate for planning purposes. You can adjust these to reflect your specific asset allocation.
How does Monte Carlo differ from the simple safe withdrawal rate?
The simple safe withdrawal rate (like the 4% rule) is derived from historical worst-case scenarios and assumes a fixed annual withdrawal. Monte Carlo explicitly models return variability year by year and shows the probability distribution of outcomes, providing more information about the range of possible results.
What is the limitation of this simulation?
This simulation assumes returns are drawn from a normal distribution, which underestimates tail risk (extreme losses). Real market returns are skewed and have fat tails. The simulation also assumes a fixed withdrawal amount, does not model inflation adjustments within simulations, and does not account for Social Security or other income sources.
Official sources
- Bengen, W.P. (1994). Determining Withdrawal Rates Using Historical Data. Journal of Financial Planning. Foundational research on safe withdrawal rates.
- SEC EDGAR: SEC Investor Tools.
- Federal Reserve: Historical interest rate data.
Reviewed by the CalculatorHub team, edited by James Graham, 14 June 2026. See our methodology.