Monte Carlo FIRE Simulator
Simulate fixed portfolio withdrawals across annual return paths. Compare the share that survive and the range of ending balances under your assumptions.
How It Works
Enter a starting portfolio, fixed annual spending, expected annual return, annual volatility, retirement horizon, and number of simulations. The default is 1,000 simulations. Each path draws independent annual returns from a normal distribution with the entered mean and standard deviation.
At the start of each simulated year, the model subtracts spending and then applies that year’s return. A path counts as a success only if the balance stays above zero through the entire horizon. The calculator displays the share of successful paths, a 95% spending estimate, final balance percentiles, and the path with the median final balance.
The success rate is the fraction of generated paths that survive. The final balance percentiles show how outcomes vary across paths, including lower ending balances that the median alone can hide.
Compare results across different spending, return, volatility, and horizon assumptions. A change in the success rate shows the model’s response to those inputs. It does not establish a probability that a real retirement plan will succeed.
Step by step
- Enter portfolio, fixed spending, return, volatility, years, and simulation count
- Review simulated survival frequency and final balance percentiles
- Change an assumption and compare the resulting model outputs
Worked example
Set portfolio to $100,000, annual spending to $10,000, expected return to 5%, volatility to 0%, years to 3, and simulations to 1,000. With zero volatility every path has the same 5% annual return.
The calculator reports a 100% simulated success rate and a final portfolio value of $82,661.25 for every path. This follows three applications of (balance minus $10,000) multiplied by 1.05. It is a deterministic arithmetic check, not a forecast.
When to use this calculator
Use this calculator to compare how fixed annual withdrawals behave across many independent return paths generated from one expected return and volatility assumption.
It is useful for comparing model scenarios. Its output depends on the assumed normal distribution and does not capture every feature of market returns or a household’s retirement income.
Common mistakes
Entering an expected return without considering whether it is nominal or real can make the result hard to interpret. Spending is fixed in nominal currency, so the model does not preserve purchasing power as prices rise.
Treating one run or the 95% spending estimate as a guarantee overstates what simulated paths show. Compare a range of assumptions and inspect the lower final balance percentiles as well as the survival frequency.
The Formula
Pₜ = (Pₜ₋₁ − S) × (1 + rₜ), where rₜ = μ + σ × Z and Z is a standard normal draw. Each path uses independent annual draws. A path succeeds if its balance remains above zero through the final year.
Spending is withdrawn before that year’s return and stays fixed in nominal terms. The default seed makes a run reproducible for the same inputs and software version. The 95% spending estimate uses a separate bounded search.
Limitations and assumptions
This model assumes independent annual returns from a normal distribution and fixed nominal spending withdrawn before growth. It does not model inflation adjusted spending, changing return regimes, links between consecutive returns, taxes, fees, other income, or flexible withdrawals. The success rate describes these simulated paths, not the chance that a real retirement plan will succeed.
Key terms
- Simulated return path
- One generated path is a series of annual returns drawn independently from the normal distribution defined by the entered mean and volatility.
- 95% spending estimate
- An annual spending estimate from a search targeting 95% simulated survival. Its limited samples and search range can affect the result; the estimate is not independently verified on a final set of paths.
- Expected return and volatility
- A model input that sets the average annual return or spread of annual returns; neither is known in advance.
Compare alternatives
Monte Carlo FIRE estimates how often simulated annual return paths avoid depletion under one fixed spending amount. Compare that result with the Dynamic SWR Calculator to model changing withdrawals, or use Sequence of Returns to compare two orderings of a preset return series.
These tools answer different questions. A Monte Carlo success rate is a frequency within the model assumptions, while the sequence calculator compares sorted versions of one selected return series. Neither result describes the future or accounts for taxes and fees.
FAQ
How does Monte Carlo simulate returns?
Each simulated year draws an independent return from a normal distribution using the expected return as its mean and volatility as its standard deviation. The calculator counts a path as successful if the balance stays above zero for the full horizon. The model does not include fat tails, changing volatility, or links between consecutive returns.
How is the 95% spending estimate calculated?
The search aims for 95% simulated survival over the entered horizon. It uses up to 500 paths per trial and searches annual spending up to 20% of the starting portfolio. Each trial uses a different sample, so the final estimate may not achieve 95% survival in a separate run. It is a noisy model estimate, not guaranteed income.
What does the success rate mean?
The displayed percentage is the fraction of simulated paths that avoid depletion under the entered assumptions, not a measured chance of your own retirement succeeding. Try several return, volatility, spending, and horizon assumptions to see how sensitive the estimate is.
What does the median trajectory show?
The median trajectory is the simulated path whose final balance ranks in the middle. The percentile figures summarize final balances across paths. Neither is a prediction for an individual portfolio, and the percentile table does not show the timing of every path’s losses.
How can I compare assumptions?
Change one input at a time, such as spending, expected return, volatility, horizon, or path count, and note how the results move. The model keeps spending fixed in nominal currency. For a separate comparison with changing withdrawals, use the Dynamic SWR Calculator; its rules are a different model.
Related Tools
Use the Dynamic SWR Calculator to model a withdrawal rule that changes with portfolio conditions, then compare sorted return orderings with Sequence of Returns. Time to FIRE can estimate an accumulation timeline before withdrawals begin. These tools use different assumptions and should be read as separate calculations.