Risk Assessment and Modelling with @RISK
- Covers the basic features and operations required to build models in Excel with @RISK.
- Learn about using key distributions, the creation and running of a model, and the interpretation of results.
- Understand the core concepts and options available to develop more sophisticated models.
- Gain experience of practical examples in cost estimation and risk registers.
- Overview. This course covers the main concepts and features necessary to build @RISK models in practice. It explains these in the context of practical models that real to real-life applications, such as cost estimation, contingency planning, risk registers, project schedule risk, cash flow analysis, valuation assessment, portfolio risk analysis, and so on. It highlights not only the use of the software per se, but also the benefits and the areas of application. The models used are carefully designed to contain many of the essential components that are necessary for many risk modelling applications, and as such the knowledge gained can be widely applied to any area of application involving risk or uncertainty assessment.
- Level and Prerequisites. The course is suitable for anyone with a basic knowledge of Excel. It brings a reader to a level to be able to use @RISK for the majority of practical modelling applications, with content that is similar to the 2-day and 3-day classroom courses that the course creator (Michael Rees) has run on hundreds of occasions over nearly 20 years.
- Follow-on courses. This course is self-contained as such. Separately, we recommend taking any of the courses in the CertFM Program to enhance one’s general modelling and data analysis skills.
- Building probabilistic models using distribution functions · Core menu icons · Simulation Settings · Outputs · Graphs · Statistics functions · Random numbers and sampling · Repeating a simulation exactly · Using multiple simulations · @RISK Goal Seek · @RISK Advanced Sensitivity Analysis
- The risk assessment process · Benefits of risk assessment · Risk identification and mapping.
- Selecting and using distributions: Uniform, Triangular, PERT, Normal, Lognormal, Bernoulli, Binomial, Discrete, Poisson · Alternative parameters · RiskTheo functions · RiskMakeInput · RiskCompound.
- RiskStatic · Function swap · Adding @RISK to existing models · Using @RISK in models containing macros.
- Dependency modelling · Correlation · Copulas · Tornado graphs · Scatter plots.
- Applications and Model Examples: Cost estimation · Risk registers · Portfolio analysis and optimisation · Business forecasting · Valuation · Random walks and time series models · Options valuation.