ModelRisk advanced course
The advanced course is aimed at participants with some previous experience of risk analysis modeling using Monte Carlo simulation. Participants will gain a stronger understanding of the techniques of risk analysis from a practical perspective, and learn more of the advanced features of ModelRisk like modeling with Objects, the user Library, and optimization. We also explain more about distributions, time series, statistics and probability calculations, and help the participant to build on their technical knowledge of risk modeling.
Day 1
- Course overview
- Quick review of risk analysis using Monte Carlo simulation
- Some quick explanations of the material covered in the Introductory class
- More on distributions
- A guide to the many distributions of ModelRisk
- How to select the right distribution
- How distributions relate to each other
- Custom formatting tool
- Format inputs/outputs automatically
- Format functions manually by category type
- More on simulation results
- Filtering simulation results + exercise
- Exporting results
- Inserting a simulated scenario back into Excel
- Exporting simulating results
- Tool for stochastic dominance tests + exercise
- The ModelRisk help file
- Structure
- Searching
- Reference system
- Videos
- Example models
- Modeling with stochastic Objects with ModelRisk
- Why Objects are a powerful approach to risk modeling
- Viewing an Object
- Simulating from an Object + exercise
- Interrogating an Object for probability calculations, etc + exercise
- Using Objects in ModelRisk functions + exercise
Day 2
- Tools for modeling extremes
- Tools for modeling distributions of minimum and maximum of a set of random variables
- Kth largest/smallest, Largest/smallest set and Range functions + exercise
- Tools for probability calculations
- Probability density/mass
- Cumulative probability
- Joint probability
- Raw and central moment
- Fitting distributions, time series and correlation
- Comparing different fitted models + exercise
- Tools for log-likelihood and information criteria for fitted models
- Bayesian model averaging + exercise
- Tools for data viewing
- Univariate sample data
- Historic time series data with video facility
- Correlation structures
- Tools for Markov Chain modeling
- Data Objects (functions to live link to databases)
- Setting up a ModelRisk Data Object within Excel
- Fitting distributions, time series, correlation to Data Objects
- Interrogating the database directly from a Data Object
- Aggregate modelling
- Aggregate modeling tools + exercise
- StopSum modeling tool + exercise
- SumProduct modeling tool + exercise
- Aggregate modeling with complex variables
Day 3
- Special distributions
- RiskEvent modeling + exercise
- Combining expert opinion + exercise
- Splice distributions
- Special tools and functions
- ModelRisk’s interpolation, numerical integration and sum functions
- Tools for moment calculations
- Non-parametric Bootstrapping tools
- Tool for reporting function parameter descriptions directly into Excel
- Tools for calculating rank order, dynamic sorting, random sampling and shuffling or arrays
- Functions for reporting simulation statistics directly into Excel
- Stochastic and deterministic optimization + exercise
- Defining decision variables
- Defining constraints
- Defining requirements
- Running an optimisation and using its results
- Matrix calculations
- Eigenvalues, eigenvectors
- Correlation matrix estimation
- Correlation matrix testing and correction
- Conversion from correlation to covariance matrices and back
Computer requirements and deliverables
Participants will need a laptop with Windows XP or Vista, Excel 2003 or later, (optionally PowerPoint 2003 or later), and ModelRisk 3.0 or later already installed. A free 30-day demo copy of ModelRisk can de downloaded here. Participants receive a CD with all example models and the PowerPoint presentations, as well as a set of printed notes for the course.
Refreshments
Lunch, morning and afternoon coffee are provided.
Testimonial
Belgium (closed) 
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