In the first three entries of this series (see links below), we discussed the use of risk analysis to:
- establish the average outcome, the average being a crucial reference quantity in decision-making in financial contexts (but a value that is not shown in a static model generally), and
- establish the variability of the outcome, such variability being of high importance in both non-financial and financial decision making in practice, and
- create a revised and more complete model including event risks, risk mitigation measures, and implied optimization of risk management.
Here we point out that risk modelling using Monte Carlo simulation can be used to capture aspects of modelling that are hard or impossible to capture using other techniques. For example:
- Dependency relationships, such as correlated sampling and parameter-dependency between distributions
- Simulation establishes the probabilities of outcomes (not just their possibility, as would a sensitivity analysis), it allows for the simultaneous variation of three or more risk factors (whereas Excel DataTables do not), and hence can deal with the large number of possible combinations for input variables.
- Some situations are inherently stochastic, and cannot be modeled using sensitivity analysis; these include especially the modelling of contingent claims (e.g. options, real options, profit share agreements, incentive schemes penalty clauses) etc.
Dr. Michael Rees
Director of Training and Consulting