This one day highly intensive course is conducted in a small group and is aimed at participants who want to learn the @RISK software and related risk analysis concepts in a rapid way and with the possibility of extensive interaction with the course tutor.

The course is hands-on, initially covering the fundamentals of @RISK, including learning to operate the software and interpret the results.  We then cover the use of a range of distributions and further @RISK modelling techniques, such as the capturing of dependency relationships in a model.  Further features of the software are also explored, and participants’ own modelling situations are discussed.

The course is taught by Dr. Michael Rees, Palisade’s Director of Training and Consulting in Europe.  Michael’s prior experience includes 15 years’ work experience as a strategy consultant, equity analyst and independent consultant.  He has a Doctorate in Mathematics and a B.A. with First Class Honours from Oxford University, and a MBA with Distinction from INSEAD.  He has studied quantitative finance with Paul Wilmott, graduating top of the class and also receiving the Wilmott Award in 2003.  He prides himself on his ability to explain complex concepts in simple, focused, intuitive, and non-mathematical ways.

Detailed Agenda

AM

• Introduction to risk analysis and simulation techniques
• Defining uncertainty through distributions: Overview
• Overview of @RISK menu items
• Example: cost estimation and budgeting using Triangular distributions
• Interpretation of simulation results 1: density and cumulative curves
• Measures of distributions (mean, standard deviation, skew etc)
• The PERT Distribution
• Use of @RISK Statistics functions (incl. RiskTheo)
• Use of multiple simulations with RiskSimtable
• RiskStatic and @RISK function swap
• Utilities and Application Settings
• Working with @RISK graphs

PM

• The Binomial, Poisson, Discrete distributions (incl. RiskCompound)
• Example: event risk modelling
• The Normal and Lognormal distributions
• Alternate parameter methods
• Interpretation of simulation results 2: tornado graphs and scatter plots
• Model auditing
• Parameter dependency and correlated sampling
• Other @RISK functionality and functions (e.g. further sensitivity analysis and reporting features, use of macros, Library, Help features)
• Open Session, according to participants’ needs: E.g. Introduction to other distributions as relevant (e.g. Geometric, Exponential, Weibull, Beta, Gamma), distribution fitting, use of the DUniform distribution, further discussion and modelling of participants’ applications
• Examples: modelling time series, cash flow modelling, extensions of cost budgeting, measuring correlations and rank correlations, comparing dependency and correlation, dependency modelling in cost budgeting, in event modelling and in time series (growth, mean-reverting and crash processes), modelling properties of functions of random variables, modelling time-to-occurrence and the number of occurrences of events

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