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@RISK 5.0
Example
Models
“@RISK 5.0 is intuitive, flexible and technically excellent. It is a quantum leap in
all aspects of functionality and user experience.” “The user interface is significantly improved in
@RISK 5.0 along with the output.” “The look and feel
of @RISK 5.0 is
a step up. Much more intuitive.
This package
seems really well put together.”
Madrid, 17 March
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» Introduction to » Risk and Decision Assessment Training using @RISK, Part I » Risk and Decision Assessment Training using @RISK, Part II




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and over 100 Example Models FREE to @RISK 5.0 Industrial — SAVE £100 Call Palisade for this offer. @RISK 5.0 Industrial — £1395 All prices include a full year of maintenance, up to £279 value. » MAINTENANCE PLAN HOLDERS upgrade to @RISK 5.0 for FREE » Download free trial All @RISK 5.0 purchases made by March 31, 2008 will be shipped with a FREE copy of Financial Models Using Simulation and Optimization by Wayne Winston and all accompanying example models. First published in 1998, this book has been updated for @RISK 5.0 in a new 3rd edition and has become the reference for business and financial modeling under uncertainty. Packed with over 100 reallife example spreadsheets, it comes with stepbystep instructions on a wide range of topics in finance and business. This offer ends March 31. +44 1895 425050  sales@palisadeeurope.com
More About @RISK 5.0 The @RISK Library
Probability distribution functions in the @RISK Library may be accessed from the Define Distribution window like any other distribution. The @RISK Library will make standardisation and consistency of analysis much easier than ever before. With the @RISK Library, workgroup efficiency will be greatly improved.
The Compound Function For example, the function: RiskCompound(RiskPoisson(5),RiskLognorm(10000,10000)) would be used in the insurance industry where the frequency or number of claims is described by RiskPoisson(5) and the severity of each claim is given by RiskLognorm(10000,10000). Here the sample value returned by RiskCompound is the total claim amount for the iteration, as given by a number claims sampled from RiskPoisson(5), each with an amount sampled from RiskLognorm(10000,10000). RiskCompound can eliminate hundreds or thousands of distribution functions from existing @RISK models by encapsulating them in a single function. The result is models that are much simpler to use, and run much faster. RiskCompound supports cell references with formulas for more complex modeling, such as accounting for the timing of claims paid.
RISKOptimizer combines the Monte Carlo simulation technology of @RISK with genetic algorithm optimisation technology to allow the optimisation of Excel spreadsheet models that contain uncertain values. Take any optimisation problem and replace uncertain values with @RISK probability distribution functions that represent a range of possible values. For each trial solution RISKOptimizer tries during optimisation, it runs a Monte Carlo simulation, finding the combination of adjustable cells that provides the best simulation results.
Asset Price Random Walks
and
Options Valuation For the case of the correlated random walks of multiple assets with a constant correlation coefficient, these can be set up using the Correlated Time Series feature of @RISK. » Download example model: AssetPrices.Options.BS.Multi.xls
Discounted Cash Flow (DCF) » Download example model: CashFlow.xls
Insurance Claims with RiskCompound » Download example model: RiskCompound.xls
Product Mix with RISKOptimizer » Download example model: ProductMix.xls
Six Sigma DOE Modeling a response based on multiple factors can often be accomplished by generating a statistically significant function through experimental design or multiple regression analysis. In this example, @RISK simulates the variation using Normal distributions for each factor. The output is Weld Strength (N) and contains a RiskSixSigma property function that includes the Lower Specification Limit (LSL), Upper Specification Limit (USL), and Target value specified. After you run the simulation, Six Sigma statistics are generated using @RISK Six Sigma functions for CpkUpper, CpkLower, Cpk, and PPM Defects (or DPM). Standard @RISK statistics functions (like RiskMean) were also used. » Download example model: SixSigmaDOE.xls
Value at Risk (VAR) » Download example model: VAR.xls More Example Models online: 

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