This blog is a continuation of the series (started a couple of months ago) which discusses different reasons to perform a risk analysis. In the first two blogs, I discussed the use of risk analysis to a) establish the average outcome, the average being a crucial reference quantity in decision-making in financial contexts, and b) establish the variability of the outcome, such variability being of high importance in both non-financial and financial decision making in practice. Risk analysis with Monte Carlo simulation can be accomplished directly in your Excel models with Palisade’s @RISK software add-in.
Here I wish to make a third point: risk analysis will generally lead to a revised (and more correct) definition of the model. It will at the same time allow any base (reference) case to be presented within the context of the full range of possible outcomes.
Examples of where differences arise in the two modelling philosophies include: 1. A situation in which there is an event risk with say 40% probability of occurrence would include this risk variable in the risk model, but typically not in a static model (its occurrence not being the most likely outcome). 2. A risk model would generally contain risk mitigation measures that make sense to conduct when the consequences of risky outcomes are considered; however, such line items may not form part of the static analysis.
The variables in the risk model will be an augmented set compared to the static mode, and ultimately, a risk model represents the residual uncertainty once all measures deemed to be appropriate have been implemented; in this sense risk modelling is inherently a process of optimization.
For more about optimization with Monte Carlo software in Excel, see RISKOptimizer.
Dr. Michael Rees
Director of Training and Consulting