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 complete scenario actually happened. The relationships between stocks, bonds, inflation, and cash returns within each calendar year are real, not modeled. When the simulation runs your plan through 1966–1995, it captures the difficult combination of high inflation and weak real returns that defined that era. Because the checked-in dataset ends in 2023, a 30-year FIREwiz window can start no later than 1994.
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, FIREwiz gets 67 complete overlapping 30-year periods. Many share years of data, so they are not independent. Difficult results tend to cluster around a small set of starting years. That tells you something useful about sequence risk, but it does not cover every future the market could produce.
Monte Carlo Simulation: What Could Happen
FIREwiz Monte Carlo takes a different approach. Instead of replaying contiguous history, it creates 500 synthetic sequences by sampling complete annual rows from the 1928–2023 dataset with replacement. Each draw keeps that year's stock, bond, cash, gold, and inflation values together.
The method can combine observed years in sequences that never occurred, expanding the range of return ordering beyond the 67 complete historical windows. Its scope is still limited to annual outcomes that appear in the source dataset; it does not invent a new return or inflation regime.
FIREwiz uses a saved random seed, so the same inputs reproduce the same draws. That makes a saved baseline useful for comparing a withdrawal rule or allocation without sampling noise changing at the same time. The current engine does not expose controls for assumed mean return, volatility, or correlation.
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.
Annual extremes and relationships. Historical simulation preserves complete multi-year sequences. FIREwiz Monte Carlo preserves each sampled year's cross-asset relationship and observed annual extremes, but breaks relationships from one year to the next.
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 results can be heavily influenced by a few difficult starting years. That clustering is informative, but a small change in the data window can move the result. Monte Carlo spreads risk across 500 sampled sequences, producing a broader estimate while obscuring the specific historical episode behind a 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 when observed annual outcomes arrive in different orders?” It broadens sequence testing, while remaining anchored to the range of years in the dataset.
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 500 sampled sequences instead of 67 complete historical windows does not make the result more accurate. The usefulness of Monte Carlo depends on its sampling assumptions and source data. More samples reduce sampling noise within that model; they do not prove that the model describes the future.
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 lets you switch between both simulation types and save a baseline before changing the method. Compare the plan-funded rate, portfolio survival, spending coverage, and ending values, then inspect the historical starting years or Monte Carlo scenario identifiers behind the result.