Question
I've seen some online retirement calculators mention that they use a Monte Carlo simulation. What is that?
Answer
A Monte Carlo simulation might sound like a ride you'd find at Disneyworld, but it's actually a statistical method used to determine probability and assess risk. At its most basic, a Monte Carlo simulation allows the user to determine the likelihood of different outcomes based on a set of assumptions and how those assumptions respond to random variables.
Origin in A-Bomb Research
The name Monte Carlo naturally brings to mind the gambling mecca located in Monaco, and its use in this case is no coincidence. Scientists working on the Manhattan Project during World War II applied the name to the simulation method used to develop the atomic bomb because of the method's use of random chance.
Since then the Monte Carlo method has been applied to everything from economics to traffic patterns. What these applications all have in common is that they apply random variables (also known as a stochastic technique) to test how a given piece of information will react under various conditions. For example, an oil company might use a Monte Carlo simulation to determine a range of potential outcomes in helping it decide whether to drill at a specific location.
Use in Financial Planning
Monte Carlo simulations are particularly useful in the realm of finance. Given the unpredictable nature of the stock market, the Monte Carlo method can help financial planners model how a particular portfolio will perform under various market conditions, thus helping them make more informed investment decisions. This approach is especially useful in retirement planning, in which investors try to figure out which savings rates, allocations, market returns and spending patterns will allow them to make their nest eggs last a lifetime.
David Blanchett, Morningstar's head of retirement research, says the Monte Carlo method has become popular with financial planners because it takes into account real-world experiences in a way that other methods that assume a given rate of return don't. "The reason Monte Carlo simulations are being used more frequently," he says, "is because they do a better job explaining the potential outcomes versus time-value-of-money calculations, such as future value. Future value will tell you the expected value of a portfolio given its present value, years to grow, potential cash flows and growth rate. The problem with a future value calculation is that it treats the outcome as certain, while in reality, and especially with the markets, nothing is certain. A Monte Carlo simulation provides a more 'colourful' perspective of the range of potential outcomes given the expected return and volatility of a portfolio."
By running thousands of scenarios using specific parameters, planners can determine the likelihood of specific outcomes. For example, let's say a planner wants to determine the odds of a whether a portfolio made up of 50% stocks and 50% bonds could last an investor for 30 years in retirement given a specified withdrawal rate. A Monte Carlo simulation would use historic market returns to simulate the portfolio's performance under a variety of conditions. In some instances the portfolio might lose money early and struggle to recover. In others it might experience early growth only to fade near the end of the 30-year time frame. In even other instances, performance may jump around like a ping-pong ball.
Individually these scenario calculations don't have much value. But when taken as a whole they paint a picture of how the portfolio is likely to perform over time. So, for example, if that 50%-stock/50%-bond portfolio is found to last in 750 out of 1,000 random scenarios, it may be said that it stands a 75% chance of lasting for 30 years in reality. That's a reasonably high probability, but some planners and their clients may aim for an even higher rate of certainty that the clients won't run out of money.
Monte Carlo Criticism
As useful as Monte Carlo simulations can be, they are far from fool-proof. Critics say they tend to understate the potential impact of major market shake-ups such as the financial crisis of 2008. In an age when many planners and investors keep a wary eye out for black swans and tail risk--financial jargon for very rare events that nonetheless do happen from time to time--a statistical model that fails to foresee such market jolts is far from ideal. Even though a Monte Carlo model might suggest a 75% probability that a retired couple won't run out of money, for example, the downside of falling short is extreme.
To address this problem, some Monte Carlo simulations are designed to allow users to use a more "fat-tailed" distribution method, meaning that the odds of extreme events are counted as being greater than they are in a traditional bell-shaped distribution curve.
Ultimately, Monte Carlo simulations are only as good as their design and the data they use. When using one of the many online retirement calculators based on the Monte Carlo method, or if your financial adviser uses a Monte Carlo simulation in shaping your retirement plan, it can't hurt to ask what assumptions are being applied and how the model deals with rare but all-too-real market jolts such as the one triggered by the financial crisis. If you're not pleased with the answer, consider using another resource more to your liking. Or, if you are computer- and maths-savvy, you can run your own Monte Carlo simulation using your home computer and spreadsheet software.
