The Monte Carlo simulation has not mistery. It means only to use A LOT of random numbers to model variables that you do not know exactly how they are. For example, the inflation rate. You can work out throught a very difficult economic theory, including monetary policies, GPD growth, interest rate policy and more elements, or you can consider that the inflation rate is a random variable which follows a normal distribution.
Why is it so important?
Because is the tool to take into account risk. If we define the entry parameters to a model (like growth rate, or stock price) as fixed-value, there is any variation in the output variables of the model and therfore, no risk (the risk of an investment is the variability of the final result). That is what is known as a deterministic model.
To take into account the risk factor, we can calculate a lot of times the final result of an investment (output variables of a finance model, often implemented in Excel) with different entry parameters. That is what is known as a stochastic or probabilistic model. It is the main use of Monte Carlo simulation in finance.
How can it be implemented?
Frequently, by modelling the entry parameters of the model as random variables, characterized by their mean and standard deviation, and calculating the ouputs of the model for each possible value of the entry parameters. With that ouput values, for example, it is possible to build a histogram of NPV (i.e. the project’s probability distribution).
The mean and the standard deviation from the entry variables are usually obtained from historical data. There is not only one way to implement the Monte Carlo method. It is more an art that a science.
The mean and the standard deviation from the entry variables are usually obtained from historical data. There is not only one way to implement the Monte Carlo method. It is more an art that a science.
To know more about the Monte Carlo method, please visit the following related links:
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