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Features within ModelRisk for Insurance and Finance

ModelRisk runs seamlessly in Excel with any Monte Carlo spreadsheet simulation package. This means that ModelRisk is complementary to tools such as Crystal Ball, @RISK. ModelRisk enables you to build robust, sophisticated, and useful financial tools in Microsoft Excel in a fraction of the time necessary using the current Monte Carlo simulation tools. Crystal Ball, @RISK allow you to run the model you construct in ModelRisk using Monte Carlo simulation and perform sensitivity analyses on the results of the ModelRisk models.

 

  • Unique approach to defining and manipulating random variables as objects allowing unprecedented flexibility in modeling insurance and finance issues;

  • Time series models like Geometric Brownian Motion, Markov Chain, ARCH, GARCH, Wilkie models and Jump Diffusion (see figure below);


  • Various elliptical and Archimedean copulas;

  • Intuitive and highly flexible tools for fitting time series, distributions and copulas to data;

  • Determination of a portfolio's efficient frontier;

  • Density, cumulative probability and generation functions for all univariate distributions and moment calculations so you can quickly and accurately perform complex probability calculations, find custom MLE's. etc;

  • Over 70 different distributions, both univariate and multivariate, used in the insurance and finance fields, with shifting and bounding (see figure below);

  • Ruin and depletion models;

  • Risk event models, allowing you to evaluate the effect of risk events;

  • Powerful, unique approach to extreme value modeling allowing you, for example, to directly calculate the probability that the largest of a million claims following a certain distribution will not exceed some value X with 95% confidence;

  • Splicing of distributions to, for example, model the bulk of a claim distribution with a Lognormal and splice a Pareto to extend the high-end tail;

  • One-click statistical analysis of data, including bootstrapping;

  • Stochastic dominance analysis;

  • Direct aggregate distribution modeling with recursive and FFT methods (Panjer method screen show in Appendix);

  • Direct determination of premiums via known identities and numerical integration;

  • Direct determination of moments of aggregate distributions and fitting various distributions to those moments;

  • All functions are optimized for both speed and accuracy;

ModelRisk is not only very user-friendly, but also teaches and helps the user employ the methods and techniques available by:

  • A specialized version of ModelAssist for Insurance and Finance (an advanced 'help file') shows how to use ModelRisk with explanation of any relevant theory, linked problems and example model solutions, videos, a search engine and much more!;

  • Sophisticated graphical interfaces to visualize, explain and insert model components in an intuitive manner;

  • All features within ModelRisk return meaningful error messages to guide the user when there has been an input error;

  • A unique translation feature means that ModelRisk models can be passed between users using different language versions of Excel.

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