On July 2, 2025, we made several important changes to our Monte Carlo simulation in order to provide you with a more accurate projection. This FAQ explains why we’ve recently updated our modeling approach. These changes are designed to give you a more realistic, transparent, and useful view of your financial future—so you can plan with more clarity and fewer surprises.
What Changes Have Been Made to Boldin’s Monte Carlo Simulation?
Each change is described in more detail below.
We are now using an AAGR (Arithmetic Mean) instead of a CAGR (Geometric Mean) when running the Monte Carlo simulation.
Normally-distributed rates of return are now 100%-correlated, meaning that within each of the 1,000 paths, all accounts go up or down in unison each month.
We’ve updated the standard deviations used with all rates of return.
CHANGE 1: We Now Use AAGR Instead of CAGR
As part of our latest model updates, we’ve adjusted how we calculate investment returns in Monte Carlo. This change improves how volatility is handled and brings our simulations more in line with real-world behavior.
Why did we switch from using CAGR (Geometric Mean) to AAGR (Arithmetic Mean)?
Using CAGR (which already builds in the long-term effect of volatility) inside a Monte Carlo simulation (which also adds volatility) means volatility gets counted twice. This made projections too conservative, especially over longer time horizons.
Think of it this way:
Arithmetic mean (AAGR):
Shows the average annual return before considering volatility
Best used in Monte Carlo simulations
Geometric mean (CAGR):
Reflects the average annual growth rate after accounting for volatility over time
Best used for long-term assumptions in linear forecasts
Helpful source: Kitces: Volatility Drag & Mean Returns
How might this change my plan results?
Using AAGR instead of CAGR could lead to an increase in your Retirement Chance of Success.
CHANGE 2: Correlation of Account Returns
Normally-distributed rates of return are now 100%-correlated, meaning that within each of the 1,000 paths, all accounts go up or down in unison each month.
Why did we change how accounts move together in our simulations?
To further improve the accuracy of our projections, we’ve updated how account returns are modeled within the simulation. Previously, the simulation treated each account as moving completely independently. This change ensures your plan reflects how portfolios typically behave in real markets—especially during periods of volatility—and helps avoid overly smooth or optimistic results.
Here’s how it works in the Boldin Planner:
Our model doesn’t yet track individual asset classes separately (like stocks vs. bonds) but rather uses a single blended rate of return (for example, 6%) and a single standard deviation (for example, 11%) to represent your holdings within each account.
In that setup, it’s standard to assume all accounts move together in simulations, since they share the same blended risk and return.
How might this change my plan results?
Plans with many accounts might see a drop in chance of success, while the impact for plans with fewer accounts is minimal as seen in the collected data below:
CHANGE 3: Refined Standard Deviations
We’ve updated the standard deviations used with all rates of return.
Why did we update our volatility (standard deviation) assumptions?
We refined the standard deviations in our Monte Carlo simulations to align with our Better Rates research, making projections more realistic by pairing each return assumption with the most up‑to‑date volatility data.
How might this change my plan results?
It depends on your return assumptions:
0–3% returns: standard deviations stayed the same
4–7% returns: small increase in standard deviations
8–10%+ returns: small decrease in standard deviations
What this could mean:
You might see a drop in chance of success if your plan uses return assumptions with higher standard deviations, leading to a wider range of outcomes (more upside, but also more downside risk).
You might see an increase if your plan uses return assumptions where standard deviations decreased, narrowing the range of possible outcomes.
How Can I Trust the Numbers If the Model Changed?
It’s a fair question—and a good one.
Financial models are not static; they evolve over time as better data, research, and tools become available. When we update our simulation methods, it’s not because the old approach was wrong, but because we’ve found a better way to reflect how markets actually behave.
These refinements are designed to make your plan more realistic, not more risky. In fact, by capturing market swings more accurately, we reduce the chance of underestimating potential challenges and help you plan with more confidence.
This is part of our commitment to keeping your plan grounded in the best available thinking. As markets and research continue to evolve, we’ll keep improving the model—so you can continue making smart, informed decisions about your future.
Additional Resources: