C**hapter 8** illustrates the use of Monte Carlo simulation in obtaining a range of values for certain financial indicators of a company of interest (e.g. profitability and borrowing).

A company's future profitability, borrowing, and many other quantities are all highly uncertain quantities. This chapter describes the theory and gives practical examples of how to run a simulation. For example, in a simulation analysis, the computer begins by picking at random a value for each of the uncertain variables based on its specified probability distribution. Monte Carlo variables assume that the processes being studied are independent of each other and that each value is a random draw from a distribution. The end result of Monte Carlo simulation is the continuous probability distribution with its own expected value and standard deviation.

Simulation relates to both sensitivity analysis (Chapter 6) and scenario analysis (Chapter 7). It ties together sensitivities and input variable probability distributions in order to give answers to questions such as: What strains on the firm's liquidity may be caused by changes in certain variables such as sales volume and credit granted to customers?

In the previous 2 chapters we examined sensitivity analysis and scenario planning as techniques to assess the risk associated with financial modelling. Although these techniques are good for assessing the effect of discrete risk they can provide little ...

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