You plug your numbers into a retirement calculator. It tells you: "You'll have $2.3 million at age 65."
Great. But will you really?
That number assumes markets return exactly 7% every single year for the next 30 years. Markets don't work that way. Some years they're up 25%. Some years they're down 35%. The order matters enormously—especially once you start withdrawing money.
This is where Monte Carlo simulation comes in.
A fixed return calculator does simple math:
Year 1: $100,000 × 1.07 = $107,000
Year 2: $107,000 × 1.07 = $114,490
Year 3: $114,490 × 1.07 = $122,504
... and so onEvery year, your portfolio grows by exactly 7%. It's deterministic—plug in the same inputs, get the same output.
Pros:
Cons:
Instead of assuming one outcome, Monte Carlo simulation runs your retirement plan through hundreds or thousands of different possible futures.
Each simulation:
Run 1,000 simulations and you might get:
That gives you a 78% success rate—a probability, not a false certainty.
Let's say you're retiring with $1 million and plan to withdraw $40,000/year (4% rule).
Fixed Return Calculator (7% average):
Monte Carlo Simulation (7% average, 15% volatility):
Same average return. Very different insights.
This is the critical insight that fixed return calculators completely miss.
Imagine two retirees, both starting with $1 million, both withdrawing $50,000/year, both experiencing an average 7% return over 20 years.
Retiree A: Bad returns early
Retiree B: Good returns early
Despite the same average return, Retiree A might run out of money while Retiree B ends with millions.
Why? Because Retiree A was selling shares at low prices to fund withdrawals. Those shares weren't there to recover when markets bounced back.
This is sequence of returns risk, and it's why Monte Carlo matters.
The percentage of simulations where you didn't run out of money.
Not just pass/fail, but the range of where you might end up:
Visual representation of uncertainty over time. The "fan" shape shows how uncertainty grows the further you project.
Monte Carlo isn't magic. It has its own issues:
The simulation is only as good as its assumptions. If you assume 10% returns with 12% volatility, you'll get different results than 6% returns with 18% volatility.
This is why assumption transparency matters. A Monte Carlo result of "92% success rate" is meaningless if you don't know what returns and volatility were assumed.
Most Monte Carlo tools use historical data. But past performance doesn't guarantee future results. The best approach: run Monte Carlo using different assumption sets (historical, BlackRock, Vanguard, GMO) and see how your success rate changes.
Here's what most people miss: the single most valuable thing Monte Carlo can show you isn't your success rate—it's how sensitive your success rate is to assumptions.
Run your plan with historical returns: 92% success
Run it with BlackRock CME: 78% success
Run it with Vanguard CME: 65% success
Run it with GMO: 48% success
Now you know something important. Your plan depends heavily on optimistic assumptions. That's not necessarily bad—but you should know it.
Fixed return calculators tell you what happens if everything goes exactly as planned.
Monte Carlo tells you what happens across a range of plausible futures.
For a decision as important as retirement, you want the range—not false precision.
QuantCalc runs your plan against published CME data from BlackRock, JPMorgan, Vanguard, and GMO, showing you exactly how sensitive your retirement is to different forecasts.
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