Different editions to suit your needs
ModelRisk Standard (STD):
provides advanced Monte Carlo simulation in Excel.
ModelRisk Professional (PRO):
adds 'objects' to ModelRisk Standard that greatly extend and simplify what you can model, as well as optimization and much more.
ModelRisk Industrial (IND):
adds a range of advanced features to the Professional version that help solve common, more complex problems specific to various industries.
Some of the key features
Monte Carlo simulation
STD - PRO - IND
ModelRisk has a comprehensive range of tools to run Monte Carlo simulations within Excel. Results are presented in a separate window that allows you to customize, save and share a comprehensive range of graphical and statistical analyses.
STD - PRO - IND
ModelRisk incorporates a truly complete range of distributions. Graphical interfaces, categorization by function, fitting to data and a detailed interactive guide on the theory and use of each distribution help ensure that you find the correct distribution for your problem. You can also create your own distribution using the shaper tool.
STD - PRO - IND
Modeling any correlated behavior between distributions is a critical component in risk analysis. ModelRisk allows the user to visualize and fit correlation structures to data through its copula tools. Through its unique approach to correlating variables, any number of distributions can be correlated. ModelRisk's own data copula offers a powerful way to replicate any unusual correlation pattern.
PRO - IND
Uniquely, ModelRisk has built-in tools for simulating time series, together with graphical interfaces and fitting to data to ensure you understand and select the right time series model. The custom time series tools also let you create your own expert-based forecasts.
PRO - IND
ModelRisk incorporates the world's leading simulation optimizer from OptTek Systems. Targets, constraints, decision variables and requirements are all defined with ModelRisk functions within the Excel spreadsheet. A graphical interface reports the optimizer's progress and allows the user to insert optimal solutions back into the model with one mouse click.
Ordinary Differential Equation tool
ModelRisk’s Ordinary Differential Equation (ODE) tool numerically evaluates a system of ordinary differential equations. The user can specify any differential equations that can be described with Excel functions. One or more time stamps (specific points in time) can be specified for the evaluation of the variable(s). The interface will plot any variable against time or any two variables together
ModelRisk’s DataObject tool allows the user to create links to unopened Excel files or various types of databases. An SQL wizard guides the user to select what is required from the database, and a preview feature shows the selected data. The data can then be used in various ways within ModelRisk, including fitting to distributions, correlation structures and time series models.
Comparison list of ModelRisk versions
|Unrestricted speed and model size|
|Monte Carlo simulation|
|Bounded, shifted distributions|
|Correlation of distributions|
|One-click function view|
|ModelRisk function search and format tool|
|Run macros before, during or after simulation|
|VBA and C++ calls to ModelRisk functions|
|Full graphical simulation reports|
|Full statistical reports|
|View simulation results statistics in spreadsheet|
|Save results in Results Viewer format|
|Conversion from other Monte Carlo add-ins|
|Full help file and example models|
|Informative error messages|
|Time series forecast|
|Fitting distributions to data|
|Fitting correlation structures to data|
|Fitting time series to data|
|Statistical fit results in spreadsheet|
|Expert elicitation tools|
|Working with ModelRisk objects|
|Markov chain tools|
|Combining expert estimates|
|Calculation of distribution moments|
|Empirical copula to reproduce any correlation pattern|
|Stop Sum and Sum Product tools|
|Risk Event tool|
|Extreme Value tool|
|Six Sigma support|
|Ordinary differential equation|
|Integration and Interpolation|
|Bayesian averaging for fitted models|
|Nested summation and product tools|
|Simulation Imported Data (SID)|