Risk and uncertainty are an inherent part of any situation in which a forecast is involved. The assessment and modelling of these may be thought of as a natural extension of regular traditional financial modelling.
Perhaps a better term for “risk” and “uncertainty” is “reality”: The modelling of risks and uncertainties is really an attempt to capture the reality of a situation better than is captured by traditional financial models in which one or only a few sensitivities or scenarios are considered, and where some risks may have been excluded entirely.
The key drivers of the need to use such “reality modelling” (risk/uncertainty modelling) include:
- Where a robust (transparent, or credible) process is required for major decisions (e.g. complex projects, legal decisions)
- Where one wishes to understand the extent to which base case plans may be biased (e.g. unintentional and/or optimism biases)
- Where there is inherently a high level of risk/uncertainty in the situation (e.g. success/fail events, non-linear payoffs)
- Where knowledge of possible ranges/probabilities of outcomes could inform decision-making (e.g. budget contingency planning)
We are involved in a wide range of projects that seek to create a better understanding of business economics when risk and uncertainty are taken into account explicitly.
In practice, the quantification of risks and uncertainties often requires the use of Monte Carlo Simulation. This can be done using Excel/VBA or with add-ins such as @RISK or ModelRisk. We are one of the world’s most experienced practitioners both in the use of VBA and in @RISK for risk modelling and Monte Carlo Simulation.
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