Monte Carlo Simulation Excel Template

Description
Usually in deterministic models, events are predicted in a simple linear system, and it is assumed that the conditions recorded initially do not change. However, if conditions do not change results will not change. However, obviously things are more complex than this and conditions do change, and events are generally determined by a complex interrelation of various variables, some of which can be nearly impossible to estimate. Monte Carlo simulations can solve this problem by utilizing probability distributions for each input variable, and then by running numerous simulations to produce likely outcomes. So, this model allows the user to predict an outcome without conducting numerous costly experiments.

To perform a Monte Carlo simulation, the steps are:

--> Definition of the mathematical formula for the outcome

--> Identification of the probability distributions of the input variables and definition of their parameters

--> Running of the simulations

--> Analyzation and optimization

If using your results for a risk analysis or a business plan is the objective, then you can stop after analyzation and optimization, however if you'd like to also optimize the outcomes, then a sensitivity analysis is necessary. In this type of analysis, the 'importance' of each input is measured, and from there you may decide to act on the ones you see as the most influential. Generally, the correlation coefficient between each input and the output is utilized, however different techniques are able to be adopted.