When you run a retirement simulation, the tool is answering a deceptively simple question: “Will my money last?” But the answer depends entirely on what market conditions you test against. The two dominant approaches — historical simulation and Monte Carlo simulation — take fundamentally different paths to get there. Understanding what each one does, and what it misses, is essential to interpreting the results.
Historical Simulation: What Actually Happened
Historical simulation takes real sequences of annual returns and runs your retirement plan through each of them. If you have data from 1928 to 2023 and you're modeling a 30-year retirement, the simulation tests every overlapping 30-year window: 1928–1957, 1929–1958, 1930–1959, and so on. Each window produces a complete scenario — a trajectory of portfolio values, withdrawals, and either survival or depletion.
The strength of this approach is that each scenario actually happened. The correlations between stocks, bonds, inflation, and cash returns are real, not modeled. When the simulation runs your plan through 1966–1995, it captures the brutal combination of high inflation and stagnant equity returns that defined that era. When it runs through 2000–2029, it captures the dot-com crash followed by the 2008 financial crisis — the kind of back-to-back stress that no one would have predicted in advance.
This matters more than it might seem. Asset classes don't behave independently. Bonds and stocks have a complicated, shifting relationship. Inflation erodes purchasing power in ways that interact with both nominal returns and withdrawal amounts. Historical simulation preserves all of these interactions automatically because it's using the actual data.
The limitation is sample size. With data from 1928 to 2023, you get roughly 65 overlapping 30-year periods. That's not a small number, but many of those periods share years of data, meaning they're not truly independent. The worst-case scenarios tend to cluster around the same handful of starting years — 1929, 1937, 1966, 2000. If your plan fails, it's almost always because one of those periods broke it. That tells you something important, but it doesn't tell you everything.
Monte Carlo Simulation: What Could Happen
Monte Carlo simulation takes a different approach. Instead of replaying history, it generates thousands of synthetic return sequences by randomly sampling from statistical distributions calibrated to historical data. A typical run might produce 10,000 simulated 30-year periods, each with its own random sequence of annual stock returns, bond returns, and inflation rates.
The power of this method is breadth. Ten thousand scenarios can explore corners of the possibility space that the historical record never visited. What if we get a decade of moderate but persistent negative real returns? What if inflation runs at 6% for 15 years instead of the 10 years we saw in the 1970s? Monte Carlo can generate these scenarios, giving you a richer sense of how your plan performs across a wide range of futures.
Monte Carlo also enables sensitivity analysis in a way historical simulation cannot. You can adjust the assumed mean return, volatility, or correlation parameters and instantly see how outcomes shift. This is valuable for stress-testing assumptions — if you believe future stock returns will be lower than the historical average, you can model that directly.
But there's a cost. Most Monte Carlo implementations assume that each year's returns are drawn independently from the others. In reality, markets exhibit mean reversion over long periods, momentum over short periods, and regime changes where the statistical character of returns shifts for years at a time. A Monte Carlo simulation can produce sequences that look nothing like real market behavior — ten consecutive years of -20% returns, say, or a decade of 30%+ annual gains. These sequences are technically possible but historically unprecedented, and including them can distort your results in both directions.
Key Differences in Practice
The two methods answer different questions, and their failure modes differ in revealing ways.
Fat tails and correlations. Historical simulation naturally captures the fat-tailed distribution of real returns — the fact that extreme years happen more often than a normal distribution would predict. It also preserves the actual correlations between asset classes. Monte Carlo can be calibrated to match these properties, but simple implementations using normal distributions will understate tail risk and may generate unrealistic cross-asset behavior.
Scenario diversity. Monte Carlo generates far more scenarios, but quantity is not the same as quality. Historical simulation's scenarios are few but real. Monte Carlo's scenarios are plentiful but synthetic. A historical success rate of 95% means your plan survived all but a few of the worst periods in modern market history. A Monte Carlo success rate of 95% means your plan survived 95% of a large set of statistically generated scenarios, some of which may be unrealistic.
Sensitivity to starting conditions. Historical simulation results are heavily influenced by a small number of bad starting years. If your plan fails in 3 out of 65 historical periods, those 3 failures are probably 1929, 1966, and 2000. This clustering is informative — it tells you exactly what kind of environment is dangerous — but it also means a small change in the data window can flip results significantly. Monte Carlo spreads risk more evenly across its thousands of scenarios, giving smoother probability estimates but potentially obscuring the specific conditions that cause failure.
The independence assumption. This is Monte Carlo's most significant weakness for retirement planning. Sequence of returns risk — the danger that poor returns early in retirement will deplete a portfolio even if average returns are fine — is the central risk that retirement simulations exist to measure. Because Monte Carlo treats each year independently, it can generate sequences where bad years cluster in ways that either overstate or understate this risk compared to how markets actually behave.
Why Using Both Matters
Each method has blind spots that the other covers.
Historical simulation tells you: “Would your plan have survived everything the market has actually thrown at retirees since 1928?” That's a powerful test. If your plan fails against the historical record, it's failing against real events, not hypothetical ones. The 4% rule, for example, was derived from historical simulation — it's the withdrawal rate that survived the worst 30-year period in the data.
Monte Carlo simulation tells you: “How does your plan hold up across a broader range of possible futures, including ones we haven't seen yet?” This is particularly valuable if you believe the future may not resemble the past — if, for instance, you expect lower returns from equities, higher inflation, or longer retirements than the historical data covers.
When both methods agree, you can have more confidence in the result. If your plan shows a 95% success rate in both historical and Monte Carlo simulations, it's robust across both real and synthetic scenarios. When they disagree, the gap itself is informative. A plan that passes historical simulation but fails Monte Carlo may be relying on favorable conditions that happened to hold in the past but aren't guaranteed. A plan that fails historical simulation but passes Monte Carlo may be tripped up by specific historical events that are unlikely to repeat in exactly the same way.
Common Misconceptions
Monte Carlo is not “more accurate” because it runs more simulations
Running 10,000 simulations instead of 65 does not make the results more reliable. The accuracy of Monte Carlo depends on the quality of its assumptions — the distributions it samples from, the correlations it models, and the independence assumption it makes. More simulations give you a more precise estimate of the wrong answer if the model is poorly specified. Historical simulation uses fewer scenarios, but each one is grounded in reality.
Historical simulation is not “outdated”
It's common to hear that historical data is irrelevant because “markets have changed.” Markets have changed in many ways — global integration, central bank policy, financial instruments — but the fundamental dynamics of risk, return, and inflation persist. The historical record includes world wars, pandemics, oil crises, the rise and fall of entire economic paradigms, and multiple periods where experts declared that “this time is different.” It remains the richest source of information about how asset classes actually behave under stress.
Neither method predicts the future
Both methods are tools for stress-testing a plan, not forecasting what will happen. A 90% success rate does not mean there is a 90% chance your retirement will go well. It means your plan survived 90% of the scenarios that method generated. The future will be its own unique sequence of returns, one that may look nothing like any historical period or Monte Carlo draw. The goal is not prediction but preparation — building a plan that is robust across a wide range of conditions.
Putting It Into Practice
The practical takeaway is straightforward: don't rely on a single simulation method. Use historical simulation to ground your planning in real-world experience. Use Monte Carlo to explore a wider range of possibilities and stress-test your assumptions. Pay attention to where the results diverge, and understand what's driving the difference.
FIREwiz runs both simulation types side by side, so you can compare historical and Monte Carlo results for the same set of inputs. You can see not just whether your plan survives, but how it behaves under different kinds of stress — the historical worst cases and the synthetic extremes. That combination gives you a clearer, more honest picture of your plan's resilience than either method alone.