Decision-Making and Uncertainty Analysis using @RISK5: Two Day Course

This two-day course aims to provide participants with hands-on experience and the analytic tools to conduct risk analysis using @RISK5, demonstrated across a range of applications.

Day 1 is focussed on the fundamentals of @RISK, including learning to operate the software and to interpret the results of the analysis. Day 2 is focussed on extending participants’ @RISK modelling techniques, including further topics in the use of distributions and the capturing of dependency relationships. 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

Day 1: Fundamentals of @RISK

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)
  • Use of @RISK Statistics functions

PM

  • Use of multiple simulations with RiskSimtable
  • The Binomial, Poisson, Discrete distributions (incl. RiskCompound)
  • Example: event risk modelling
  • RiskStatic and @RISK function swap
  • Utilities and Application Settings
  • Working with @RISK graphs
  • The PERT Distribution
  • The Normal and Lognormal distributions
  • Alternate parameter methods

Day 2: Building Robust Models with @RISK

AM

  • RiskTheo functions
  • Interpretation of simulation results 2: tornado graphs and scatter plots
  • Model auditing
  • Introduction to other distributions as relevant (e.g. Geometric, Exponential, Weibull, Beta, Gamma)
  • Distribution fitting
  • Use of the DUniform distribution
  • Examples: modelling time series, cash flow modelling, extensions of cost budgeting, modelling properties of functions of random variables, modelling time-to-occurrence and the number of occurrences of events

PM

  • 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: Further discussion and modelling of participants’ applications
  • Examples: 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)


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