Aim and audience
The aim of this tutorial is to provide an overview of Monte Carlo Simulations, what they are, when and why they are used and how to implement them using functions available in Excel.
Monte Carlo simulation is a type of simulation that relies on repeated random sampling and statistical analysis to compute results. The random nature of Monte Carlo simulations implies that the result is not (precisely) known in advance and therefore it represents a complex form of ‘what-if’ analysis. Monte Carlo analysis essentially tries to capture systematic variation and the implications of this variation.
In most modelling the input is fixed (the model is deterministic in nature) and therefore it is not necessarily taking account of the systematic variations that might arise in some of the inputs.
Scenario analysis (i.e. worst case, base case, best case) brokers this issue to an extent, but has some disadvantages; the most significant when compared to Monte Carlo simulation is statistical rigour.