Understanding Boldin’s Monte Carlo Simulation: What It Is, Why It Matters, and What’s New

At Boldin, we’re committed to helping you make smart, confident financial decisions. One of the key tools we use to support that goal is a Monte Carlo simulation—a powerful way to model financial uncertainty and stress-test your retirement plan.

Boldin’s Monte Carlo simulation has recently been updated to better reflect real-world uncertainty. This FAQ explains what changed, why we made the updates, and how they may affect your plan.

What Are Monte Carlo Simulations? 

Monte Carlo simulations model many possible future outcomes by running thousands of trials with randomized monthly returns. The goal is to understand the range and probability of different results over time, an important goal when it comes to long-term financial planning.

After all, when planning, there is no way to predict one outcome that we know will happen. With Monte Carlo, you can assess a range of possible outcomes. 

How Do Monte Carlo Simulations Differ from Linear Simulations? 

When projecting your financial future, you can use either linear or Monte Carlo simulations.

• Linear simulations assume a fixed return each year based on long-term averages. They’re simple, easy to follow, and useful for setting expectations—but they don’t reflect real-world variability.
• Monte Carlo simulations introduce randomness to returns, modeling real uncertainty and showing a range of outcomes instead of a single path.

We recommend using both: linear for clarity, and Monte Carlo for realism. Together, they provide a more complete picture of your financial plan.

What’s Changed in Boldin’s Monte Carlo Simulation? 

We have made three important updates to our Monte Carlo simulation in order to provide you with a more accurate projection. 

1. Switched from using CAGR (Compound Annual Growth Rate) to AAGR (Arithmetic Average Growth Rate)  
2. Updated how accounts move together in simulations 
3. Refined our standard deviation assumptions

Each change is described in more detail below. 

How Do These Updates Make Your Plan Stronger?

Financial models evolve as better research, tools, and data become available. These updates don’t mean the old approach was wrong—they represent improvements that more accurately reflect how markets behave.

They also reflect our commitment to keeping your plan grounded in the best available thinking. As the financial landscape continues to evolve, we’ll keep refining the model, so you can make smart, informed decisions with greater confidence.

How Does Monte Carlo Relate to My Chance of Retirement Success Score?

Your Chance of Retirement Success score is powered by Monte Carlo simulations. These simulations model thousands of possible futures to estimate how likely your plan is to succeed, based on factors like spending, market returns, and life expectancy. 

Rather than a pass/fail grade, think of your score as a probability of needing to make adjustments. For example, a 60% score means that in 6 out of 10 simulated scenarios, your plan stayed on track, while in 4 out of 10, you may need to make changes along the way.

This score helps you understand where your plan stands today and how resilient it might be to future uncertainty.

• See this detailed article for further guidance on interpreting your score as part of your ongoing planning.

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UPDATE 1: AAGR Instead of CAGR for Forecasting (A Smarter Foundation)

We are now using an AAGR (Arithmetic Mean) instead of a CAGR (Geometric Mean) when running the Monte Carlo forecast.

Why: To avoid double-counting volatility, ensuring more realistic projections.

Impact on Plan Results: A potential increase to your Retirement Chance of Success.

Why We Made This Change

Boldin’s Monte Carlo simulations used to rely on Compound Annual Growth Rate (CAGR) to model future returns. While CAGR is useful for summarizing long-term performance, it already includes the effect of volatility drag—the reduction in growth caused by year-to-year fluctuations. When used in Monte Carlo simulations, which also introduce volatility, this meant volatility was being counted twice, resulting in overly conservative projections.

To improve accuracy, we’ve switched to using Arithmetic Average Growth Rate (AAGR)—a simple average of annual returns without compounding or built-in volatility. This lets the Monte Carlo engine do its job: adding realistic variability across thousands of simulated paths.

Why AAGR is a better fit for Monte Carlo:

• AAGR gives a clean starting point, then simulations apply volatility.
  
• CAGR already bakes in volatility drag, so adding more distorts the results.
  
• This change avoids double-counting and better reflects how markets behave.

By using AAGR, Boldin’s simulations offer a more transparent, realistic view of possible outcomes, helping you plan with greater clarity and confidence.

A Helpful Analogy

One of our team members recently went on a backpacking trip. The first two days involved steep, rocky terrain with a slow pace of about 1.5 mph. On the third day, the trail flattened, and the pace increased to around 4 mph.

If you looked at the overall average speed—2 mph—you wouldn’t understand the reality of the trip. That average smooths over the ups and downs.

• CAGR is like that overall average—it tells you the final result, but not what the journey felt like.
• AAGR is like tracking the pace each day—it better captures the variability.

If they had planned their campsite locations based on a consistent 2 mph pace, they would have ended up sleeping in the wrong spots each night.

That’s the problem with using CAGR in simulations—it smooths over the very risks you need to plan for.

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Update 2: Accounts Returns Now Move Together

Normally-distributed random rates of return are now 100%-correlated, meaning that within each of the 1000 paths, all accounts go up or down in unison each month.

Why: To better reflect real-world scenarios, where market movements generally impact all accounts in the same direction each month.

Impact on Plan Results: Plans with many accounts might see a drop in the chance of success, while the impact for plans with fewer accounts is minimal.

Why We Made This Change

To further improve the accuracy of our projections, we’ve updated how account returns are modeled within the simulation. 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.

Previously, the simulations of each account were independent. That meant that your IRA could experience a bear market or boom in one year, and your Roth could experience it another.

In the enhanced model, all accounts increase or decrease in the same month, and the rate of return and standard deviation determine the magnitude of the increase and decrease of each account in the simulation.

This means that if your Rollover IRA has a conservative asset allocation and your Roth IRA has an aggressive allocation, the increases and decreases would occur at the same time, but the Roth IRA changes would be greater.

How This Works in the Boldin Planner

Our model doesn’t yet track individual asset classes separately (like stocks vs. bonds) but rather allows you to enter a single blended rate of return (for example, 6%), resulting in a single standard deviation (for example, 11%) to represent your holdings within each account. In that setup, the blended risk and return (i.e. the blended rate of return and associated blended standard deviation) is already taking into account the lower volatility of bonds relative to stocks, for projections or simulations.

How Might This Change Your Plan’s Results?

The impact of this update depends on how many accounts are in your plan:

• If you have many accounts, you might see a slight drop in your Chance of Retirement Success. That’s because the previous model treated each account as moving independently, which understated overall portfolio risk.
• If you have fewer accounts, the change is likely minimal, as your plan was already capturing a more realistic picture of market behavior.

This update doesn’t add new risk—it simply reflects how your full portfolio is likely to move together in the real world.

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CHANGE 3: We Refined the Standard Deviations Delivering More Realistic Volatility Assumptions

We’ve updated the standard deviations used in our Monte Carlo simulations to better reflect current market research and improve the accuracy of our projections.

Why This Matters: This refinement builds on our recent Better Rates update and ensures that every return assumption is paired with the most realistic volatility data available. Accurate standard deviation inputs are essential for producing simulations that closely mirror how investments actually behave, especially over long time horizons.

Impact on Your Plan Results: Changes in standard deviation can shift your Chance of Retirement Success score:

• Higher standard deviations mean more potential volatility. This can widen the range of simulated outcomes and lower your success score due to increased downside risk.
  
• Lower standard deviations narrow the range of outcomes, potentially boosting your score by reducing risk variability.
  

What Is Standard Deviation?

Standard deviation is a measure of how much investment returns tend to vary from the average over time. In the context of Monte Carlo simulations, it represents the potential ups and downs your portfolio could experience in a given year.

In short, standard deviation is one of the key ways we model uncertainty. By refining these inputs, we help ensure that your plan reflects not just expected growth, but also the realistic range of outcomes you might face in retirement.

How Might the Change to Our Standard Deviation Impact Plan Results?

It depends on your assumed rate of return:

• 0–3% returns: No change in standard deviation
  
• 4–7% returns: Small increase in standard deviation
  
• 8–10%+ returns: Small decrease in standard deviation

As a result:

• You may see a decrease in your chance of success if you’re using moderate return assumptions due to slightly higher volatility.
  
• You may see a slight increase if you’ve chosen more aggressive return assumptions where volatility was adjusted downward.

These refinements aren’t meant to make your plan look better or worse—they’re designed to make it more honest and helpful, so you can build a strategy that’s resilient to the real-world ups and downs of financial markets.

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Has Your Chance of Retirement Success Changed?

While your Chance of Retirement Success score is just one tool in your planning toolbox, it’s a powerful way to gauge your plan’s resilience. These changes help ensure that your score reflects not just the math, but the real uncertainty of life.

Log in to the Boldin Planner to assess your Chance of Retirement Success and other ways to measure your future financial success.