Monte Carlo Retirement Simulation Calculator
Monte Carlo simulation runs hundreds of randomized return sequences — including the bad luck of a 2000–2009 sequence in your first retirement years — and reports the fraction that end with money still in the portfolio. This is how professional financial planning software (MoneyGuidePro, eMoney, RightCapital) models retirement risk. A fixed-scenario calculator tells you what happens at 4.5% real returns. Monte Carlo tells you your probability of surviving all the sequences history might throw at you.
What Monte Carlo simulation actually does
Every projection of your retirement rests on an assumption about market returns. A deterministic calculator picks one number — say 4.5% real per year — and shows you exactly where your portfolio ends up. The problem: markets don't deliver 4.5% every year. They deliver 22% one year, −18% the next, 11% after that. The sequence of those returns — not just the average — determines whether you run out of money.1
Monte Carlo simulation solves this by running the math hundreds of times, each time drawing a different random sequence of annual returns from a realistic statistical distribution. Some simulations get a 2000–2002 market crash in year 1 of retirement (terrible for sequence risk). Others get a strong bull market in years 1–3 (greatly improves survival odds). The percentage of simulations that end with the portfolio still positive is your probability of success.
Interpreting your success probability
There is no universal "right" target. Your appropriate success probability depends on how much spending flexibility you have and what a failed sequence actually means for you.
| Success probability | What it means | Who this fits |
|---|---|---|
| 90–100% | Conservative — leaves substantial bequest potential; portfolio rarely depletes even in poor sequences | Retirees who cannot cut spending; large bequest goals; long horizon uncertainty (age 55–60 retiring) |
| 80–90% | Moderate — the "4% rule" zone. Historically strong success rate; still leaves recovery options if early sequence is rough | Most retirees; some spending flexibility; Social Security + portfolio blend |
| 70–80% | Elevated risk — meaningful probability of portfolio depletion before plan end. Requires flexibility to cut spending if early returns are poor | Strong floor income (pension + SS covers essential expenses); genuinely flexible discretionary budget |
| Below 70% | High depletion risk — more than 1-in-3 simulations exhaust the portfolio before the plan end. Likely requires structural change: reduce withdrawal, add guaranteed income, or shorten horizon assumption | May be acceptable only with very robust guaranteed income covering essentials and discretionary portfolio used for extras only |
Note that 100% is not the goal for most retirees. A plan optimized for 100% success will leave a median ending balance equal to or larger than the starting portfolio — meaning you systematically under-spent. The "right" target is the highest withdrawal rate that keeps success probability in your comfort range, not the lowest withdrawal rate that makes the number green.
What drives success probability most
1. Withdrawal rate — the dominant factor
The ratio of your net annual portfolio draw to the starting portfolio value drives success probability more than any other single variable. At a 30-year horizon with typical 60/40 return assumptions:
- 3.0% withdrawal rate → ~97–100% success (very conservative)
- 3.5% withdrawal rate → ~93–96% success
- 4.0% withdrawal rate → ~88–92% success (Bengen's 4% rule range)
- 4.5% withdrawal rate → ~80–86% success
- 5.0% withdrawal rate → ~70–78% success
These ranges come from both the Trinity study historical data2 and Monte Carlo modeling with realistic 60/40 assumptions. Social Security and pension income directly improve these numbers by reducing your net portfolio draw — which is why optimizing SS claiming timing and building an income floor are high-value retirement income decisions.
2. Volatility creates sequence-of-returns risk
Two portfolios with identical average real returns but different volatility produce very different success rates. Higher volatility means a bad early sequence can permanently impair the portfolio — drawdowns early in retirement force you to sell assets at low prices, and those shares don't participate in the recovery.3 This is why a bond tent, bucket strategy, or Guyton-Klinger guardrails can raise success probability even without changing the average return assumption — they dampen the volatility effect on the withdrawal pool.
3. Horizon — every decade matters
A 20-year plan succeeds at rates ~8–12 percentage points higher than a 30-year plan at the same withdrawal rate. A 35-year or 40-year plan (for couples with one partner age 55–60) is considerably more demanding. If you're modeling a 35-year horizon and see a concerning success rate, the most impactful lever is often delaying Social Security to age 70 — the 76% higher benefit vs. age 62 compounds over 30+ years and dramatically reduces the net draw on the portfolio starting at 70.
Historical benchmarks — Trinity study
Cooley, Hubbard, and Walz (1998) tested fixed withdrawal rates against 65 years of actual U.S. market return data, examining what fraction of all 20-, 25-, and 30-year historical periods produced portfolio survival.2 Subsequent updates (2011, 2020) extended the dataset.
| Withdrawal rate | 20-year success | 25-year success | 30-year success | 35-year success |
|---|---|---|---|---|
| 3.0% of initial portfolio | 100% | 100% | 100% | ~100% |
| 3.5% | 100% | 98% | 96% | ~91% |
| 4.0% (Bengen rule) | 100% | 98% | 95% | ~89% |
| 4.5% | 98% | 94% | 87% | ~82% |
| 5.0% | 96% | 89% | 80% | ~74% |
| 5.5% | 93% | 84% | 73% | ~67% |
60/40 portfolio, inflation-adjusted withdrawals. Based on Cooley, Hubbard & Walz (1998) and subsequent updates. Historical data reflects U.S. market returns; future returns may differ. 35-year column from Kitces (2012) extension of the Trinity study.
Monte Carlo vs. deterministic scenarios
The Retirement Income Sustainability Calculator on this site shows you three fixed-return scenarios: conservative (3% real), moderate (4.5%), and growth (6%). Those numbers give you a clean, deterministic picture of each scenario — useful for understanding how return assumptions affect outcomes.
Monte Carlo trades that clarity for realism. Instead of asking "what if returns are always 4.5%?", it asks: "what if I got −18% in year 1, then +22%, then +8%, then −12%… across hundreds of different sequences drawn from a realistic distribution?" The fixed scenarios will always show more orderly trajectories than the Monte Carlo fan because they average out the turbulence. The fan chart's width — the gap between the 10th and 90th percentile lines — is a direct visual measure of sequence-of-returns risk in your specific plan.
Integrating Monte Carlo into a retirement income plan
- Safe withdrawal rate context: The 4% rule is a historical heuristic from a single-pass deterministic analysis. Monte Carlo gives you the probability distribution behind that heuristic and lets you adjust inputs to find your comfort zone.
- Guyton-Klinger guardrails: Dynamic withdrawal rules (cut 10% when your withdrawal rate rises too high; increase 10% when markets are strong) can raise Monte Carlo success probability by 5–10 percentage points vs. rigid spending, because they allow the portfolio to recover from rough sequences before depletion occurs.
- Variable Percentage Withdrawal: VPW's automatic proportional adjustment is the most responsive dynamic strategy — spending contracts immediately with the portfolio rather than waiting for a guardrail trigger. This effectively eliminates depletion risk but requires genuine spending flexibility.
- Bond tent / rising equity glide path: Temporarily overweighting bonds at retirement reduces volatility during the highest-risk sequence window (years 1–10), which directly improves Monte Carlo success probability without changing the expected return assumption much.
- Bucket strategy: A cash/near-cash buffer covering 1–3 years of expenses reduces sequence risk by allowing you to avoid selling equities at depressed prices during a crash — the bucket acts as a volatility absorber for the growth portfolio.
- Social Security timing: Every dollar of SS income reduces your net portfolio draw. Delaying SS to 70 raises the guaranteed monthly benefit by up to 76% vs. age 62 — and that benefit is inflation-indexed for life, the most powerful hedge against both longevity risk and sequence-of-returns risk simultaneously.
Turn your Monte Carlo results into a plan
Knowing your success probability is the beginning, not the end. The decisions that move the needle — which accounts to draw from, when to claim Social Security, whether a bond tent or a SPIA floor makes more sense for your situation, how much Roth conversion to do before RMDs — require coordinating all the levers at once. That's where a retirement income specialist earns their fee.
Sources
- Bengen, W. P. (1994). Determining Withdrawal Rates Using Historical Data. Journal of Financial Planning, 7(4), 171–180. The foundational analysis establishing the 4% safe withdrawal rate using historical U.S. market return sequences — the first systematic demonstration that sequence of returns, not just average returns, determines portfolio survival.
- Cooley, P. L., Hubbard, C. M., & Walz, D. T. (1998). Retirement Savings: Choosing a Withdrawal Rate That Is Sustainable. AAII Journal. The "Trinity Study." Tests withdrawal rates from 3%–12% against 65 years of historical stock and bond data. The 30-year success-rate table in this guide is based on this paper and subsequent 2011 and 2020 updates. Available via AAII.com.
- Pfau, W., & Kitces, M. (2014). Reducing Retirement Risk with a Rising Equity Glide Path. Journal of Financial Planning, 27(1). Demonstrates that sequence-of-returns risk is highest in the years immediately surrounding retirement and that bond-heavy allocations at that moment improve success probability — the basis for the bond tent / rising equity glide path strategy.
- Kitces, M. (2012). What Returns Are Safe Withdrawal Rates Based Upon? Kitces.com. Extends the Trinity study to 35- and 40-year horizons and discusses how Monte Carlo simulation compares to historical scenario analysis. Includes the 35-year success-rate data referenced in the benchmarks table. Available at kitces.com.
Monte Carlo calculations use lognormal returns with drift correction: μ = ln(1+r) − σ²/2, where r is your expected real return and σ is annual volatility. No tax or IRS regulatory values are used — this is pure portfolio probability math. Historical return/volatility reference values: 60/40 portfolio ~4.5% real / ~11% SD; 80/20 ~5.5% / ~14%; 40/60 ~3% / ~8%. Verified May 2026 against Vanguard long-run return estimates and Pfau (2014) data.
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