The Monte Carlo Simulation

by Craig Moser on February 21, 2020

The Monte Carlo simulation models the probability of different outcomes with random variables.  It helps determine the impact of risk and uncertainty when forecasting.  When using any Monte Carlo simulation (online or otherwise) you must also understand assumptions made by the specific tool being used.


Does the simulation use the historical mean return for each asset class?  Does it make a downward adjustment for today’s fairly low interest rates and high valuations?  What are the distribution assumptions?  (If the Monte Carlo simulation assumes normal distribution for stock returns, risk may be significantly understated.)

What about reversion to the mean?  Does the simulation assume several bad years in a row increase the likelihood of the next year being good?  Simulations that don’t include this type of assumption have fewer very bad scenarios than ones that don’t.  While it may look good on paper, it may not provide the most accurate results possible.


It may be obvious the timeline associated with a simulation directly impacts the results.  Accepting that, do you know what mortality assumptions are made?  Does the age at death fluctuate?  Is the scenario using a fixed length of time?  The answers to questions like these help you more fully understand and interpret results.


Some Monte Carlo simulations may not take unexpected expenses into consideration.  It’s fine if they don’t, but you need to be aware of that fact.  Unexpected expenses such as a major health event can have unintended outcomes and possibly result in running out of money sometime in the future.

Clearly stated, if you don’t understand the assumptions, it’s difficult to get much value from the simulations.  Because those assumptions directly impact your results, take the time to ask the right questions.

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