Monte Carlo Retirement Calculator
Run a 1,000-trial Monte Carlo simulation of your retirement portfolio. See success probability (the percentage of trials where money lasts), median ending balance, worst-case scenarios, and how spending changes the success rate.
What Is a Monte Carlo Simulation?
A Monte Carlo simulation runs thousands of randomized iterations of a retirement portfolio, drawing each year's investment return from a probability distribution (typically normal or log-normal) defined by an expected return and standard deviation. By sampling 1,000+ random sequences of returns, the simulation produces a probability distribution of outcomes — what percentage of trials end with money still in the portfolio (success), what the median outcome looks like, what the worst 10% of outcomes look like (downside risk), and what the best 10% look like (upside potential). Per SEC investor education on retirement planning, Monte Carlo is the most widely-used method for stress-testing retirement plans because it explicitly accounts for sequence-of-returns risk in a way that average-return calculations cannot. Vanguard, Fidelity, Schwab, and most fee-only advisors use Monte Carlo as their primary planning tool.
How to Read Monte Carlo Results — The 80% Rule
Most planners target a Monte Carlo success probability of 80-90%. A 95-100% success rate suggests you're under-spending and could afford more in retirement; below 70% suggests material risk of running out of money and warrants either reducing withdrawals, working longer, or annuitizing a portion. The exact target depends on flexibility — if you can cut spending in bad years (Guyton-Klinger guardrails) or have other income (Social Security, pension, part-time work), you can target lower success rates because you have a margin to adjust. If your withdrawals are inflexible (fixed expenses, no other income), aim for 90%+ success. Don't aim for 100% success — that requires either ultra-low withdrawals (well below 4% rule) or massively over-saving, both leaving large unspent balances at death rather than enjoying retirement. Per Kitces research, the 100%-success target reflects a behavioral failure to balance "running out of money" risk against "running out of life" risk.
The Limitations of Monte Carlo
Monte Carlo has three main weaknesses to understand: (1) Independence assumption — most simple Monte Carlos assume each year's return is independent of the prior year's, but real markets exhibit serial correlation (mean reversion, momentum) that simple Monte Carlo ignores. More sophisticated implementations use bootstrap sampling from historical data instead. (2) Distribution choice — assuming normal distribution underestimates tail risk; real returns have "fat tails" with more extreme outcomes than normal distribution predicts. The 2008 crash was a 4-standard-deviation event under normal distribution but happened in real life. (3) Static spending — most Monte Carlos assume rigid inflation-adjusted withdrawals, but retirees actually adjust spending in bear markets (Guyton-Klinger guardrails) and respond to portfolio performance. Dynamic withdrawal Monte Carlos produce 5-15 percentage point higher success rates than static ones at the same starting withdrawal rate.
Monte Carlo Retirement Worked Example 2026 — $1M Portfolio at 4%
Here is a concrete Monte Carlo run for a 65-year-old single retiree using current 2026 capital market assumptions. Inputs: $1,000,000 starting balance, $40,000 annual withdrawal (the 4% rule), 30-year horizon, 60/40 portfolio (60% US equities, 40% bonds), expected real return 4.5%, standard deviation 11%. Result (1,000 trials): 87% success rate. Median ending balance: $1.4 million. 10th percentile (downside): $0 at year 28. 90th percentile (upside): $4.2M. Sensitivity: dropping the withdrawal to $35,000 (3.5%) lifts success to 95%; raising it to $50,000 (5%) drops success to 58%. A more conservative 50/50 portfolio with the same $40K withdrawal hits ~82% success but with a tighter range. Per the SSA actuarial life table, a 65-year-old has a median remaining lifespan of ~18 years (male) or ~21 years (female), so a 30-year horizon is conservative for most singles but appropriate for couples (joint life expectancy adds 5-7 years). Updated 2026-06-20.
Monte Carlo vs Historical Simulation vs Safe Withdrawal Rate
Three methods exist for retirement stress-testing: Monte Carlo (random sampling from distribution, 1,000+ trials, produces probability), historical simulation (run the plan against every actual rolling 30-year window from 1926-2026, see how many would have succeeded), and safe withdrawal rate (find the maximum withdrawal that historically would have survived the worst 30-year period, e.g., Bengen's 4% rule). Historical simulation captures real-world serial correlation but is limited to ~30 actual non-overlapping cohorts. Monte Carlo captures more theoretical scenarios but may miss real-world dynamics. Safe withdrawal rate is conservative because it's based on the single worst historical case (1966 retirees). Most rigorous planning uses all three together, looking for consistent recommendations across methods.
Sequence-of-Returns Risk — Why This Monte Carlo Retirement Calculator Beats Straight-Line Math
Sequence-of-returns risk is the single reason a Monte Carlo retirement calculator produces different — and more honest — answers than a compound-interest spreadsheet. Two retirees with identical 30-year average returns can end up with wildly different outcomes if one hits a bear market in years 1-5 while the other hits it in years 25-30. The first retiree is selling shares at low prices while withdrawing, permanently locking in losses; the second retiree rides out early gains before the drawdown. A straight-line calculator using a fixed 7% return will report both scenarios as identical; Monte Carlo, by sampling year-by-year returns from a distribution, reveals the 20-30 percentage-point spread in success rate.
The Federal Reserve's 2022 Survey of Consumer Finances reports the median retirement account balance for near-retirees (55-64) is roughly $185,000 — an order of magnitude below what a 4% withdrawal at $60K income replacement requires. Running your own numbers through this Monte Carlo retirement calculator with realistic 2026 volatility assumptions (equity σ ≈ 15-17%, bond σ ≈ 5-7%, correlation ≈ 0.2) will typically shave 8-15 percentage points off the success rate a straight-line calculator would give you. That gap is exactly why fee-only planners charge for Monte Carlo output rather than back-of-envelope math — the sequence risk is invisible to averages.
Updated 2026-07-08. Source: Federal Reserve Board — Economic Well-Being of U.S. Households, Retirement Section.
Frequently Asked Questions
What is a Monte Carlo retirement simulation?
A Monte Carlo simulation runs 1,000+ randomized trials of your retirement portfolio, drawing each year's return from a probability distribution defined by expected return and volatility. The output is a probability of success — the percentage of trials where money lasts the full retirement period.
What success probability should I target?
Most planners target 80-90% Monte Carlo success. Below 70% suggests material risk of running out — reduce spending, work longer, or annuitize. Above 95% may indicate over-saving or under-spending. Targeting 100% requires unrealistic over-saving and leaves large unspent balances at death.
How is Monte Carlo different from the 4% rule?
The 4% rule is based on a single worst historical case (typically 1966 retirees). Monte Carlo runs thousands of randomized scenarios and produces a probability distribution. Monte Carlo can model any spending pattern, asset allocation, or expected return, while the 4% rule is fixed at one withdrawal rate.
Why does volatility matter so much in Monte Carlo?
High volatility produces a wide range of outcomes — both excellent and disastrous. Sequence-of-returns risk amplifies during withdrawal periods, so volatility plus withdrawals causes Monte Carlo to fail trials that an average-return calculation would predict succeed. A 7% return at 20% volatility looks much riskier than 7% at 10% volatility.
Should I use real or nominal returns?
Use real (inflation-adjusted) returns paired with real (today's dollar) withdrawals — simpler and avoids inflation modeling errors. Or use nominal returns paired with inflation-adjusted withdrawals (the calculator handles both). Mixing real returns with nominal withdrawals (or vice versa) produces wrong results.
What's the difference between Monte Carlo and historical backtesting?
Monte Carlo samples random returns from a probability distribution. Historical backtesting runs the plan against every actual historical 30-year window (1926-1996, 1927-1997, etc.). Monte Carlo is more flexible but assumes returns are independent year-to-year; historical backtesting captures real serial correlation but has only ~30 non-overlapping cohorts.
How many trials should a Monte Carlo retirement simulation run?
1,000 trials is the practical minimum and gives roughly ±1.5 percentage points of accuracy on the success rate. 10,000 trials tightens that to ±0.5 points but rarely changes a planning decision because input uncertainty (expected return, volatility) dominates simulation noise. Vanguard and Fidelity use 10,000 trials in client-facing tools; for personal planning, 1,000 is sufficient. Running 100 trials is too few — the success rate jumps around enough that two runs with the same inputs can show 80% vs 86%.
What success rate does Vanguard and Fidelity target for retirement plans?
Vanguard targets 80% success in its standard planning tool, treating below 80% as needing plan adjustment. Fidelity targets 90% in its Retirement Score tool, with green-light only above 95% and adjustment recommended below 80%. Most fee-only advisors use a band of 80–90% as "on track" — neither so high that the client over-saves nor so low that material shortfall risk remains. The right target also depends on your spending flexibility — if you can cut 10–20% in bad markets, an 80% Monte Carlo plan effectively becomes ~90%+ in practice.
What is sequence-of-returns risk in a Monte Carlo retirement calculator?
Sequence-of-returns risk is the danger that poor investment returns in the first 5-10 years of retirement — while you are also withdrawing — permanently damage your portfolio. Two retirees with identical 30-year average returns can end up wildly different if one hits a bear market early and the other hits it late. A straight-line calculator hides this risk because averages are the same. Monte Carlo exposes it by sampling year-by-year returns, which is why the same 4% withdrawal can show 60% success on a bad sequence and 99% success on a good sequence with identical average return assumptions.
How can I improve my Monte Carlo retirement success rate above 90%?
Five levers in order of impact: (1) delay retirement by 2-3 years — every extra year of contributions plus one fewer year of withdrawals often lifts success by 5-8 points; (2) cut the withdrawal rate from 4% to 3.5% — this alone typically adds 8-12 points; (3) hold a 2-year cash bucket at retirement to avoid selling stocks in bear markets, cutting sequence risk; (4) shift to a bond tent (rising equity glidepath) which peaks bond allocation at retirement then rises equity share back over 10 years; (5) commit to spending flexibility — a "guardrails" rule cutting withdrawals 10% in years where the portfolio drops below a threshold typically converts an 80% Monte Carlo plan to 92%+ real-world.