Decision-Making and Uncertainty Analysis using @RISK 5.0

This two-day course aims to provide participants with hands-on experience and the analytic tools to conduct risk analysis using @RISK 5.0, 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 the building of robust models with @RISK, including the use and selection of distributions and the capturing of dependency relationships in the 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

Day 1: Fundamentals of @RISK

AM

  • Introduction to risk analysis and simulation techniques
  • Defining uncertainty through distributions: Overview
  • Overview of @RISK menu items
  • Hands-on 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)
  • Interpretation of simulation results 2: tornado graphs and scatter plots
  • Use of multiple simulations with RiskSimtable
  • Hands-on example: variations of cost estimation model

PM

  • The Binomial, Poisson, Discrete distributions
  • Hands-on example: event risk modelling
  • The PERT Distribution
  • Use of @RISK Statistics functions
  • Utilities and Application Settings
  • Working with @RISK graphs

Day 2: Building Robust Models with @RISK

AM

  • Approaches to select an appropriate distribution
  • The Normal and Lognormal distributions
  • Alternate parameter methods
  • Introduction to other distributions as relevant (e.g. Geometric, Exponential, Weibull, Beta, Gamma)
  • Distribution fitting with BestFit
  • Use of the DUniform distribution
  • Hands-on 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
  • Selecting an appropriate relationship
  • Hands-on 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)
  • RiskStatic and @RISK function swap
  • Other @RISK functionality and functions (e.g. Reports in Excel, Library, Help features, RiskTheo, RiskCompound, further sensitivity analysis features, model auditing, using macros with @RISK and the @RISK macro language etc.)
  • Open Session: Further discussion and modelling of participants’ applications


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