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

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

Day 1 covers the fundamentals, including learning to operate the software and to interpret the results of the analysis.  Hands-on examples used include basic cost modelling and event risk modelling.

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.  Hands-on examples include an extension of cost budgeting, cash flow modelling, and time series modelling, with some time available to discuss participants’ own modelling applications.

The course is taught by Dr. Michael Rees, Palisade’s Global Director of Training and Consulting.  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.  He is the author of Financial Modelling in Practice (John Wiley & Sons, 2008).

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
  • Introduction to distributions and key terminology (mean, percentiles)
  • Example: cost estimation and contingency planning using Triangular distributions
  • Running a simulation and the interpretation of results 1 (density and cumulative curves)
  • Repeating a simulation (incl. random number methods, no. of iterations etc)
  • Use of @RISK Statistics functions

PM

  • The Discrete, Binomial and Poisson distributions (incl. RiskCompound)
  • Example: event risk modelling and scenario modelling
  • Example: Use of multiple simulations (examples in risk mitigation and parameter dependency)
  • RiskStatic and @RISK function swap
  • Utilities and Application Settings
  • Working with @RISK graphs
  • Working with simulation data

Day 2: Building Robust Models with @RISK

AM

  • Measures of distributions: further topics (standard deviation, skew, kurtosis)
  • The RiskTheo statistics functions
  • The PERT distribution
  • Alternate parameter methods
  • The Normal and Lognormal distributions
  • Overview of selected other distributions (e.g. Geometric, Exponential, Weibull)
  • Distribution fitting
  • Use of the DUniform distribution
  • Examples: extensions of cost budgeting, modelling time series, cash flow modelling, modelling time-to-occurrence and the number of occurrences of events, and other properties of functions of random variables

PM

  • Parameter dependency and correlated sampling
  • Interpretation of simulation results 2: tornado graphs and scatter plots
  • Other @RISK functionality and functions (e.g. report generation, model auditing, further sensitivity analysis and reporting features, use of macros, Library, Help features)
  • 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)
  • Open Session: Further discussion and modelling of participants’ application

 


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