FIRE basics
The 4% rule describes a historical average. Your actual risk can be estimated with a simulation that uses your portfolio, withdrawal rate and starting point.
The real question
The 4% rule is based on historical averages. It does not say how likely your portfolio is to last with your withdrawal rate, start date and allocation. An average compresses many possible paths into one number.
Definition
Ruin risk is the probability that a withdrawal plan exhausts its capital before the end of the planned lifetime. A 90% success rate means that nine out of ten simulated scenarios succeeded and one did not. This lets you compare plans rather than repeating the vague statement that a rule “usually worked”.
Why averages mislead
Two portfolios can have the same average return over 30 years and end very differently depending on when good and bad years occur. A crash during the first three to five years of retirement is much more dangerous than the same crash near the end because withdrawals are already taking money out while the portfolio is down. This is sequence-of-returns risk.
A serious estimate
A Monte Carlo simulation generates many different return paths, each with its own sequence of good and bad years. Success probability is the share of paths in which capital lasts. That gives a concrete estimate instead of “it should be enough”.
In practice
The FIRE calculator simulates 10,000 possible market paths from your saving phase, withdrawal rate and German tax inputs, then reports a success rate. This lets you compare personal risk with a historical average.
Interpretation
There is no universal target rate. It depends on how much uncertainty you personally accept. The value of the metric is that it gives a concrete basis instead of a generic rule of thumb.
Simulation flow
A scenario starts with your inputs. Year by year it generates return, inflation, withdrawal and the new portfolio balance. In a good year, capital may grow despite withdrawals; in a bad year it falls. The loop continues until the planned period ends or the capital is exhausted.
If 9,200 of 10,000 runs succeed, 92% is the result. It does not mean your personal life has exactly an 8% chance of failure. It means the plan failed in 800 simulated paths under the chosen assumptions.
Inputs
Ten thousand scenarios sound precise, but repetition does not make a bad model good. If spending is too low, taxes are missing or the withdrawal rate does not fit the portfolio, the calculator simply repeats the wrong plan.
Input quality matters more than the last decimal place of the success rate.
What you can change
A low success rate is not a judgement about you. It says the current combination of capital, saving phase, spending and withdrawal rate often falls short. Change one lever at a time and see which has the largest effect.
Compare several variants and choose changes you could actually maintain.
Limits of the method
The simulation creates paths from the distributions and assumptions you provide. It knows no political crisis, personal illness or panic decision. Historical returns do not repeat on a fixed calendar, so the success rate is guidance under conditions, not a law of nature.
Two runs with the same inputs can differ slightly when new random paths are drawn. With 10,000 runs the rate should not jump wildly, but a small difference is normal. The method is most useful for comparison: if a small change in spending, withdrawal rate or starting age moves the rate sharply, you know where the plan is sensitive.
Enter saving and withdrawal assumptions, then see the success rate across 10,000 simulated scenarios.
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