Fat-tail-aware return distribution with a left-tail skewness factor, bracket-based tax modeling per country, and a probability of success — not a single deterministic line.
Run my Monte CarloThree modeling choices set Monte Carlo retirement simulators apart from each other. Here is how Retirement Lab handles each:
Lognormal returns without a skewness adjustment. A clean lognormal draw produces too few of the steep, fast drawdowns that real markets deliver. Retirement Lab applies a left-tail skewness factor to its return draws, stretching the worst shocks further into the negative so simulated drawdowns reflect fat-tail risk rather than a textbook bell curve.
Flat-rate or no tax modeling. Subtracting one effective tax rate from gross withdrawals hides bracket effects, capital-gains treatment, and country-specific wealth taxes. Retirement Lab applies bracket-based income tax, capital-gains tax, and (where applicable) wealth tax inside each simulated year, for the residency country you choose, so the after-tax draw reflects the bracket you would actually retire into.
Hiding sequence-of-returns risk behind percentile aggregates. A summary “85% success” number can come from very different distributions of unlucky early-year sequences. Retirement Lab plots percentile bands (10/25/50/75/90) across the full horizon — computed from all 10,000 paths — alongside a sample of individual simulated timelines, so the impact of a bad first decade shows up as a widening fan in the early years instead of being averaged into one number.
The methodology page documents the return-distribution and tax-modeling parameters in full.
Each scenario runs 10,000 independent simulations. Every path draws its own annual market returns from the configured distribution, so the result is a probability distribution of outcomes rather than a single projection. The path count is fixed at 10,000 on the free tier; the Pro tier exposes a higher-resolution mode for users who want tighter percentile estimates.
Each year’s return for each asset is drawn around your configured mean and volatility, then shaped by a skewness and tail-weight adjustment (a Tukey g-and-h distribution) fitted to that asset’s history. For stocks the result is a longer left tail: crash years are more common than a clean bell curve predicts, while the average return and volatility stay as configured. The mean and volatility are scenario inputs you set before each run; the return shapes are fitted to history, each scenario's Portfolio page can make markets calmer or rougher, and the methodology page documents both and how they were derived.
Taxes are computed inside each simulated year, not as a flat haircut on the final balance. For the residency country you choose, the simulator applies bracket-based income tax to taxable withdrawals, capital-gains tax to realized gains, and wealth tax where the country has one. Dual taxation for U.S. citizens abroad is modeled with a simplified foreign-tax-credit overlay at the income-category level. That means the after-tax cash you can actually spend in a given year reflects the bracket the simulated portfolio puts you in that year — a high-withdrawal year and a low-withdrawal year are taxed differently, the way they would be in real life. The methodology page lists the country-by-country defaults and the explicit assumptions for regimes like NHR, IFICI, non-dom, and PFU.
Probability of success is the share of simulated paths in which the portfolio is not depleted before the end of the planning horizon, given your inputs. It is a simulation output, not a recommendation — an “85% success” result means 85 of every 100 simulated futures finished with a non-zero balance under the assumptions you provided. A different inflation, return, tax, or spending input will produce a different number; the metric is only as honest as the inputs it summarizes.
Retirement Lab publishes the simulations behind its modelling, with the household assumptions, the tax year, and the underlying data stated so the numbers can be checked.