Monte Carlo simulation is a mathematical technique which is used to estimate the possible outcomes of an uncertain event.
It's a chance to see into the future.
And while actual time travel is still beyond us, let's address three questions about Monte Carlo simulations to get you on your way to making better decisions.
Come on in, guys.
So, number one, how do they work?
Monte Carlo simulation works by modeling the probability of different outcomes in a process or system that cannot easily be predicted due to the intervention of random variables.
And it uses something called random sampling.
And random sampling is used to generate multiple possible outcomes and calculate the average result.
So take, for example, the calculation of the probability of rolling two standard dice.
Well, if you wanted to calculate this probability, the brute force way, you would have to roll the dice a whole bunch, say 36,000 times if we consider that there are six sides to a dice.
We have two of them.
And we want to run this a thousand times to get a good sample size.
But with a monte Carlo simulation, we can reduce the number of rolls by randomly sampling the possible outcomes, knowing there are 36 combination of dice rolls and calculating the percentage of times that we get, say, a seven.
Now, number two, who uses them?
There are a number of common applications for Monte Carlo simulations and perhaps the most well-known opposes in the area of just portfolio management and also in the area of investment planning.
By running thousands or even millions of simulations, investors can get a better idea of how their portfolio might perform under different market conditions.
And other common applications are things like risk analysis, option pricing and planning for spare capacity.
But a monte Carlo simulation is applied in all sorts of fields from medicine all the way through to astrophysics, all the way through, to figuring out what today's wordle might actually be.
Okay, number three How to Run one?
Monte Carlo techniques involve three basic steps.
First, you set up the predictive.
Model.
And this is identifying both the dependent variable to be predicted and the independent variables, also known as the input risk of predictive variables that will drive the predictions.
Secondly, you specify the probability distribution.
And that's the probability distribution of the independent variables.
You can use historical data or an analyst's subjective judgment to define a range of likely values and assign probability weights for each.
And then number three, we can run.
Simulations repeatedly generating random values of the independent variables.
Do this until enough results are gathered to make up a representative sample of the infinite number of possible combinations.
You can run as many Monte Carlo simulations as you wish by modifying the underlying parameters you use to simulate the data.
However, you'll also want to compute the range of variation within a sample by calculating the variance and the standard deviation which are commonly used measures of spread.
The more you sample, the more accurate your sampling range, and then the better your estimation.
And while you may not be able to travel into the future with Monte Carlo simulation, you'll have a much better idea about the possibilities that the future holds.
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