45 Probability Distributions with easy-to-use interface, running Super Speed Simulation (thousands of trials in a few seconds) with Comprehensive Statistics and Reporting, Distributional Correlations with Copulas, Latin Hypercube and Monte Carlo Simulation, Truncation, Percentile Alternate Parameters and Percentile Fit, Linking capabilities, Multidimensional Simulations, Simulation Profiling, 6 random number generators, and Risk Simulator functions in Excel

Bootstrapping, Check Model, Cluster Segmentation, Comprehensive Reports, Data Extraction and Statistics Report, Data Import, Deseasonalization and Detrending, Detailed Data Diagnostics, Distributional Fitting (Single, Multiple, Percentile Fit), Distributional Probabilities (PDF, CDF, ICDF), Hypothesis Testing, Overlay Charts, Principal Component Analysis, Sensitivity Analysis, Scenario Analysis, Statistical Analytics, Structural Breaks, Tornado and Spider Charts

Box-Jenkins ARIMA, Auto ARIMA, Basic Econometrics, Auto Econometrics, Combinatorial Fuzzy Logic, Cubic Spline, Custom Distributions, GARCH, J Curve, S Curve, Markov Chain, Maximum Likelihood, Multiple Regression, Neural Network, Nonlinear Extrapolation, Stochastic Processes, Time-Series Decomposition, Trendlines

Static, Dynamic and Stochastic Optimization with Continuous, Discrete and Integer Decision Variables, Efficient Frontier, Genetic Algorithm, Linear and Nonlinear Optimization, Single Variable Goal Seek

- Windows 10 or Windows 11 (32 and 64 bits)
- Excel 2016, 2019, 365 (32 and 64 bit)
- Microsoft .NET 2.0 and 3.0, 3.5 or later
- 850MB Hard Drive space
- Administrative Rights to install software

MAC OS users can run the software as long as they have Bootcamp, Virtual Machine, or Parallels

The following lists the new enhancements and tools available in the latest version of Risk Simulator, as well as enhancements from previous versions. Upgrades from versions 3.X, 4.X, 5.X, 6.X, or 2010.X require a small upgrade fee of only $300 (click on Purchase for more information).

- GARCH Models: GARCH, GARCH-M, TGARCH-M, TGARCH, EGARCH, EGARCH-T, GJR GARCH, GJR TGARCH
- MLE-LIMDEP (Maximum Likelihood Estimation for Limited Dependent Variables): Logit, Probit, Tobit
- Data Deseasonalization
- Data Detrending and Cyclicality Analysis
- Principal Component Analysis
- Structural Break Analysis
- Checking model integrity
- Trendline Forecasts (linear, nonlinear polynomial, power, logarithmic, exponential, moving average)
- Stepwise Multiple Regression (forward, backward, correlation, forward-backward)
- T-Copulas and Quasi-Normal Copula, Normal Copula for correlated simulations
- 6 Random Number Generators:
- ROV Advanced Subtractive Generator
- Subtractive Random Shuffle Generator
- Long Period Shuffle Generator
- Portable Random Shuffle Generator
- Quick IEEE Hex Generator
- Basic Minimal Portable Generator

- Latin Hypercube Sampling (complements existing Monte Carlo Simulation)
- Genetic Algorithm in optimization
- Single Variable Goal Seek
- Combinatorial Fuzzy Logic Forecasts
- Neural Network Forecasts (linear, nonlinear logistic, hyperbolic tangent, and cosine)

ROV Decision Tree is used to create and value decision tree models. Additional advanced methodologies and analytics are also included: Decision Tree Models, Monte Carlo Risk Simulation, Sensitivity Analysis, Scenario Analysis, Bayesian (Joint and Posterior Probability Updating), Expected Value of Information, MINIMAX, MAXIMIN, Risk Profiles.

uses percentiles and optimization to find the best-fitting distribution.

runs 45 probability distributions, their four moments, CDF, ICDF, PDF, charts, and overlay multiple distributional charts, and generates probability distribution tables.

A new module called ROV BizStats, which is a standalone tool that can be used to run basic to advanced statistical analyses at very high speeds. This new module includes the following 265+ methods:

AI Machine Learning: Bagging Linear Fit Bootstrap Aggregation (Supervised), AI Machine Learning: Bagging Nonlinear Fit Bootstrap Aggregation (Supervised), AI Machine Learning: Classification Regression Trees CART (Supervised), AI Machine Learning: Classification with Gaussian Mix & K-Means (Unsupervised), AI Machine Learning: Classification with Gaussian SVM (Supervised), AI Machine Learning: Classification with K-Nearest Neighbor (Supervised), AI Machine Learning: Classification with Phylogenetic Tree & Hierarchical Clustering (Unsupervised), AI Machine Learning: Classification with Linear SVM (Supervised), AI Machine Learning: Classification with Polynomial SVM (Supervised), AI Machine Learning: Custom Fit Model (Supervised), AI Machine Learning: Dimension Reduction Factor Analysis (Unsupervised), AI Machine Learning: Dimension Reduction Principal Component Analysis (PCA), AI Machine Learning: Ensemble Common Fit (Supervised), AI Machine Learning: Ensemble Common Fit (Supervised), AI Machine Learning: Ensemble Time-Series (Supervised), AI Machine Learning: Linear Fit Model (Supervised), AI Machine Learning: Logistic Binary Classification (Supervised), AI Machine Learning: Multivariate Discriminant Analysis (Linear) (Supervised), AI Machine Learning: Multivariate Discriminant Analysis (Quadratic) (Supervised), AI Machine Learning: Neural Network (Cosine Hyperbolic Tangent), AI Machine Learning: Neural Network (Hyperbolic Tangent) (Supervised), AI Machine Learning: Neural Network (Linear) (Supervised), AI Machine Learning: Neural Network (Logistic) (Supervised), AI Machine Learning: Normit Probit Binary Classification (Supervised), AI Machine Learning: Random Forest (Supervised), AI Machine Learning: Segmentation Clustering (Unsupervised), ANCOVA (Single Factor Multiple Treatments), ANOVA (MANOVA General Linear Model), ANOVA (Randomized Blocks Multiple Treatments), ANOVA (Single Factor Multiple Treatments), ANOVA (Single Factor Repeated Measures), ANOVA (Two-Way Analysis), ANOVA (Two-Way MANOVA General Linear Model), ARIMA, ARIMA Seasonal (SARIMA), Auto ARIMA, Auto Econometrics (Detailed), Auto Econometrics (Quick), Autocorrelation and Partial Autocorrelation, Autocorrelation Durbin-Watson AR(1) Test, Bonferroni Test (Single Variable with Repetition), Bonferroni Test (Two Variables with Repetition), Box–Cox Normal Transformation, Box’s Test for Homogeneity of Covariance, Charts: 2D Area, Charts: 2D Bar, Charts: 2D Column, Charts: 2D Line, Charts: 2D Pareto, Charts: 2D Point, Charts: 2D Scatter, Charts: 3 Variables Bubble, Charts: 3D Area, Charts: 3D Bar, Charts: 3D Column, Charts: 3D Line, Charts: 3D Pareto, Charts: 3D Point, Charts: 3D Scatter, Charts: Box-Whisker, Charts: Fan Chart, Charts: Q-Q Normal, Coefficient of Variation Homogeneity Test, Cointegration Test (Engle-Granger), Combinatorial Fuzzy Logic, Control Chart: C, Control Chart: NP, Control Chart: P, Control Chart: R, Control Chart: U, Control Chart: X, Control Chart: XMR, Convolution Simulation: Discrete Normal with Lognormal Arithmetic Scale , Convolution Simulation Discrete Normal Lognormal Logarithmic Scale, Convolution Simulation Poisson Frechet, Convolution Simulation Poisson Gumbel Max, Convolution Simulation Poisson Lognormal Arithmetic Scale, Convolution Simulation Poisson Lognormal Log Scale, Convolution Simulation Poisson Normal, Convolution Simulation Poisson Pareto, Convolution Simulation Poisson Weibull , Correlation Matrix (Linear, Nonlinear), Covariance Matrix, Cox Regression, Cubic Spline, Custom Econometric Model, Data Analysis: Cross Tabulation, Data Analysis: New Values Only, Data Analysis: Pivot Table, Data Analysis: Subtotal by Category, Data Analysis: Unique Values Only, Data Descriptive Statistics, Deseasonalize, Discriminate Analysis (Linear), Discriminate Analysis (Quadratic), Distributional Fitting: ALL: Continuous, Distributional Fitting: Continuous (Akaike Information Criterion), Distributional Fitting: Continuous (Anderson–Darling), Distributional Fitting: Continuous (Kolmogorov–Smirnov), Distributional Fitting: Continuous (Kuiper’s Statistic), Distributional Fitting: Continuous (Schwarz/Bayes Criterion), Distributional Fitting: Discrete (Chi-Square), Diversity Index (Shannon, Brillouin, Simpson), Eigenvalues and Eigenvectors, Endogeneity Test with Two Stage Least Squares (Durbin-Wu-Hausman), Endogenous Model (Instrumental Variables with Two Stage Least Squares), Error Correction Model (Engle-Granger), Exponential J-Curve, Factor Analysis (PCA with Varimax Rotation), Forecast Accuracy (All Goodness of Fit Measures), Forecast Accuracy: Akaike, Bayes, Schwarz, MAD, MSE, RMSE, Forecast Accuracy: Diebold–Mariano (Dual Competing Forecasts), Forecast Accuracy: Pesaran–Timmermann (Single Directional Forecast), Generalized Linear Models (Logit with Binary Outcomes), Generalized Linear Models (Logit with Bivariate Outcomes), Generalized Linear Models (Probit with Binary Outcomes), Generalized Linear Models (Probit with Bivariate Outcomes), Generalized Linear Models (Tobit with Censored Data), Granger Causality, Grubbs Test for Outliers, Heteroskedasticity Test (Breusch–Pagan–Godfrey), Heteroskedasticity Test (Lagrange Multiplier), Heteroskedasticity Test (Wald–Glejser), Heteroskedasticity Test (Wald’s on Individual Variables), Hodrick-Prescott Filter, Hotelling T-Square: 1 VAR with Related Measures, Hotelling T-Square: 2 VAR Dependent Pair with Related Measures, Hotelling T-Square: 2 VAR Indep. Equal Variance with Related Measures, Hotelling T-Square: 2 VAR Indep. Unequal Variance with Related Measures, Internal Consistency Reliability: Cronbach’s Alpha (Dichotomous Data), Internal Consistency Reliability: Guttman’s Lambda and Split Half Model, Inter-rater Reliability: Cohen’s Kappa, Inter-rater Reliability: Inter-Class Correlation (ICC), Inter-rater Reliability: Kendall’s W (No Ties), Inter-rater Reliability: Kendall’s W (with Ties), Inter-rater Reliability: Kuder Richardson, Kendall’s Tau Correlation (No Ties), Kendall’s Tau Correlation (with Ties), Linear Interpolation, Logistic S-Curve, Mahalanobis Distance, Markov Chain, Markov Chain Transition Risk Matrix, Multiple Poisson Regression (Population and Frequency), Multiple Regression (Deming Regression with Known Variance), Multiple Regression (Linear), Multiple Regression (Nonlinear), Multiple Regression (Ordinal Logistic Regression), Multiple Regression (Through Origin), Multiple Regression (Two-Variable Functional Form Tests), Multiple Ridge Regression (Low Variance, High Bias, High VIF), Multiple Weighted Regression (Fixing Heteroskedasticity), Nominal Data Contingency Analysis (McNemar’s Marginal Homogeneity), Nonparametric: Chi-Square GOF for Normality (Grouped Data), Nonparametric: Chi-Square Independence, Nonparametric: Chi-Square Population Variance, Nonparametric: Cochran’s Q (Binary Repeated Measures), Nonparametric: D’Agostino–Pearson Normality Test, Nonparametric: Friedman’s Test, Nonparametric: Kruskal–Wallis Test, Nonparametric: Lilliefors Test for Normality, Nonparametric: Mann–Whitney Test (Two Var), Nonparametric: Mood’s Multivariate Median Test, Nonparametric: Runs Test for Randomness, Nonparametric: Shapiro–Wilk–Royston Normality Test, Nonparametric: Wilcoxon Signed-Rank Test (One Var), Nonparametric: Wilcoxon Signed-Rank Test (Two Var), Parametric: One Variable (T) Mean, Parametric: One Variable (Z) Mean, Parametric: One Variable (Z) Proportion, Parametric: Power Curve for T-Test, Parametric: Two Variable (F) Variances, Parametric: Two Variable (T) Dependent Mean, Parametric: Two Variable (T) Independent Equal Variances, Parametric: Two Variable (T) Independent Unequal Variances, Parametric: Two Variable (Z) Independent Means, Parametric: Two Variable (Z) Independent Proportions, Partial Correlations (Using Correlation Matrix), Partial Correlations (Using Raw Data), Principal Component Analysis, Process Capability (CPK, PPK), Quick Statistic: Absolute Values (ABS), Quick Statistic: Average (AVG), Quick Statistic: Count, Quick Statistic: Difference, Quick Statistic: LAG, Quick Statistic: Lead, Quick Statistic: LN, Quick Statistic: LOG, Quick Statistic: Max, Quick Statistic: Median, Quick Statistic: Min, Quick Statistic: Mode, Quick Statistic: Power, Quick Statistic: Rank Ascending, Quick Statistic: Rank Descending, Quick Statistic: Relative LN Returns, Quick Statistic: Relative Returns, Quick Statistic: Semi-Standard Deviation (Lower), Quick Statistic: Semi-Standard Deviation (Upper), Quick Statistic: Standard Deviation Population, Quick Statistic: Standard Deviation Sample, Quick Statistic: Sum, Quick Statistic: Variance (Population), Quick Statistic: Variance (Sample), ROC Curves, AUC, and Classification Tables, Seasonality, Segmentation Clustering, Skew and Kurtosis: Shapiro–Wilk and D’Agostino–Pearson, Specifications Cubed Test (Ramsey’s RESET), Specifications Squared Test (Ramsey’s RESET), Stationarity: Augmented Dickey-Fuller, Stationarity: Dickey-Fuller (Constant and Trend), Stationarity: Dickey-Fuller (Constant No Trend), Stationarity: Dickey-Fuller (No Constant No Trend), Stepwise Regression (Backward), Stepwise Regression (Correlation), Stepwise Regression (Forward), Stepwise Regression (Forward-Backward), Stochastic Process (Exponential Brownian Motion), Stochastic Process (Geometric Brownian Motion), Stochastic Process (Jump Diffusion), Stochastic Process (Mean Reversion), Stochastic Process (Mean Reverting and Jump Diffusion), Structural Break (Chow Structural Stability Test), Survival and Hazard Tables (Kaplan–Meier), Time-Series Analysis (Auto), Time-Series Analysis (Double Exponential Smoothing), Time-Series Analysis (Double Moving Average Lag), Time-Series Analysis (Double Moving Average), Time-Series Analysis (Holt–Winters Additive), Time-Series Analysis (Holt–Winters Multiplicative), Time-Series Analysis (Seasonal Additive), Time-Series Analysis (Seasonal Multiplicative), Time-Series Analysis (Single Exponential Smoothing), Time-Series Analysis (Single Moving Average), Trend Line (Difference Detrended), Trend Line (Exponential Detrended), Trend Line (Exponential), Trend Line (Linear Detrended), Trend Line (Linear), Trend Line (Logarithmic Detrended), Trend Line (Logarithmic), Trend Line (Moving Average Detrended), Trend Line (Moving Average), Trend Line (Polynomial Detrended), Trend Line (Polynomial), Trend Line (Power Detrended), Trend Line (Power), Trend Line (Rate Detrended), Trend Line (Static Mean Detrended), Trend Line (Static Median Detrended), Value at Risk (VaR and CVaR), Variances Homogeneity Bartlett’s Test, Volatility (EGARCH), Volatility (EGARCH-T), Volatility (GARCH), Volatility (GARCH-M), Volatility (GJR GARCH), Volatility (GJR TGARCH), Volatility (Log Returns), Volatility (TGARCH), Volatility (TGARCH-M), Yield Curve (Bliss), Yield Curve (Nelson–Siegel)

Risk Simulator 2012 now supports 11 languages: English, French, German, Italian, Japanese, Korean, Portuguese, Simplified Chinese, Traditional Chinese, Spanish and Russian, complete with the localized languages’ user interface, user manuals, reports, examples, exercises, tools, charts, and more!

You can now toggle between global view and regular view on the forecast charts where all of the controls from the regular tabbed view are now available in a single comprehensive global view.

You can now copy and paste multiple cells with noncontiguous assumptions and forecasts.

The ultra powerful Distributional Analysis tool now has a facelift where you can run PDF, CDF, ICDF and combinations of these on a data grid, copy the computed data, and view different chart types.

The Edit Correlation between input assumptions tool now supports an interactive visual correlation chart.

This new version supports Windows 7, 8, 10 – 32 and 64 bit versions, and Excel 2013, 2016 – 32 bit and 64 bit versions.

This version comes with 140 pages of step-by-step hands-on exercises for running each of the techniques and tools in Risk Simulator and 103 pages of probability distribution details (describing the characteristics and nature of the 45 distributions available in Risk Simulator), complementing the 206-page detailed user manual (translated into 11 languages).

The Troubleshooter tool is now integrated in the Start menu for Risk Simulator, where you can use this tool to obtain the status of Risk Simulator install (e.g., registry settings, COM settings, installation path), restore Risk Simulator if it is disabled, fix Excel security settings, obtain Hardware ID, and more.

This new version includes quicker report generation, scatter plot support in the Overlay Charts tool, simplified drop-list for seasonality selection in the Time Series Forecasting tool, more elegant looking Spider Charts, auto installation of licenses (if you upgrade from the same major version such as from 2012(A) to 2012(B), where the previous license will now be automatically transferred to the new version; this does not apply if you are upgrading from a different major version such as from version 5.X to 2010.X), and many other small enhancements and fixes.

General Enhancements in Risk Simulator Version 5.4 and beyond:

This new capability allows you to run simulations at super speeds (50X to 500X faster depending on the model and your processor speed!) by first analyzing your Excel model and then compiling the model into pure mathematical code and running the simulations at very high speeds. Certain models that cannot be compiled will be run at regular speed (e.g., models with VBA functions and macros, links to external data or files, unsupported or wrong functions, and errors in the model).

We added a new high-speed engine in our forecast methods and analytical tools. Same analyses results as in previous versions but they now run 10X to 30X faster depending on the analysis and your processor speed!

Users with both Excel 2007 and Excel 2003/XP loaded can now easily and seamlessly run Risk Simulator on any Excel versions in a single computer. There is now an Excel version switching tool that allows you to determine which version of Excel to start Risk Simulator with.

You can turn cell comments on or off in Risk Simulator’s Options menu, to decide if you wish to show cell comments on all input assumptions, output forecasts and decision variables.

You can now access all of Risk Simulator’s tools and menus using a mouse right click.

You can now enter in a single equation or multiple equations to run basic econometrics modeling and automatically create tens to hundreds of iterations of the same model through predefined variables as well as shift the data over time.

Simply click on the Advanced button when you run Optimization and select super speed simulation.

Users with Excel 2010/2007 will see a completely reworked icon toolbar that is more intuitive and user friendly. There will be four sets of icons that fit most screen resolutions (1280 x 760 and above).

The forecast charts now have additional graphical enhancements.

If you perform some data filtering in the forecast chart (Data Filter section in the Options tab), the Statistics tab will show the updated statistics based on the truncated data set. If you do not truncate the forecast data, the full dataset’s statistics will be shown as usual.

This is a new tab within the forecast chart whereby you can modify the existing forecast charts including performing distributional fitting on the forecast data set, creating overlay PDF/CDF/ICDF charts, overlaying scatter charts, and changing chart options (chart types, 3D rotation, colors, zoom, tilt, number of decimals, minimum and maximum values to chart on the axes, title name, and many other options, including the ability to save the revised settings and print the chart in various formats).

This new forecasting tool is used to run hundreds and even thousands of model combinations and permutations using smart heuristics to determine the best-fitting model for your data, by testing linear, nonlinear, lagged, lead, interacting, nested, and other models. This tool is the counterpart to the ROV Risk Modeler software’s detailed autoeconometrics, which is capable of running hundreds of thousands to a few million models on large datasets.

The existing forecasting tool is now enhanced with new capabilities including the ability to create new variables and functions such as TIME (a linear time-series variable), DIFF (first differencing the time-series data set), RESIDUAL (data from the error term of a forecast equation you specify), RATE (first order ratio of time-series data), and FORECAST (data from the error term of a forecast equation you specify).

This new tool runs the most common trendlines including linear, nonlinear, exponential, power, moving average, and polynomial models. It returns a series of charts as well as the goodness-of-fit statistics for each model.

This new charting tool is used to compare multiple assumptions and/or forecast variables, by plotting them in a time-series or cross-sectional overlay manner. This allows you to quickly view the similarities and differences in the assumptions and forecasts in easy to read charts.

This tool is capable of segregating and clustering or grouping a large set of data into different natural statistical groups by applying some smart algorithms and heuristics.

This new tool can create reports of just the key forecast statistics (e.g., mean, median mode, standard deviation, variance, coefficient of variation, skew, kurtosis) as well as confidence levels and probabilities of the output forecast variables you select. The result is a comparison table listing the selected statistics across multiple forecasts.

There are several new enhancements to our licensing:

Vista users with the user access control turned on or limited users without administrative logins will still be able to enjoy the full functionalities of Risk Simulator by being able to install the software licenses without doing any additional work. To install a new license file you received, simply start Excel, click on Risk Simulator, License, Install License and browse to the license file you are provided to activate the software permanently or for a longer trial period).

Risk Simulator now has the capability of turning on or off certain functionalities to allow you to customize your risk analysis experience. For instance, if you are only interested in the forecasting tools in Risk Simulator, you may be able to obtain a special license that activates only the forecasting tools and leaves the other modules deactivated, thereby saving some costs on the software. The four modules that can be turned on or off are Simulation, Forecasting, Optimization and Analytical Tools. In addition, specific tools in each module can also be turned on or off. This customization is only available for site licenses of more than 10 computers.

Together with the new forecasting tools and techniques in version 2012, Risk Simulator now has the following forecasting methodologies:

ARIMA (Autoregressive Integrated Moving Average)

- Auto ARIMA
- Auto Econometrics
- Basic Econometrics
- Combinatorial Fuzzy Logic
- Cubic Spline
- GARCH (Generalized Autoregressive Conditional Heteroskedasticity)
- J-Curves
- Markov Chains
- Maximum Likelihood
- Neural Network
- Nonlinear Extrapolation
- Regression
- S-Curves
- Stochastic Processes
- Time-Series Analysis
- Trendlines

You can now access Risk Simulator functions within Excel by clicking on Insert Function anywhere in your spreadsheet and scrolling to the functions starting with RS. Here, you can set assumptions as well as obtain the forecast statistics of a forecast variable. For instance, you can run the RSAssumptionNormal function to set a normal distribution assumption to a cell, or RSForecastStatistic to obtain the statistics of a forecast cell. In the set assumption forecast, you can set the placeholder or temporary value that is seen before and after a simulation is run (Value), the name of the assumption (Variable Name), and the parameters of the distribution (e.g., Mean, Standard Deviation), as well as other items such as percentile values, correlations, as well as min and max boundaries. For the results, you can also use RSForecastStatistic(A1, “Percentile99.9”) to obtain the 99.90 percentile value of cell A1, where this cell has a forecast parameter set. The functions that can be used include: “PercentileXXX”, “CertaintyXXX”, “Mean”, “Median”, StandardDeviation”, “Variance”, “Skewness”, and “Kurtosis”.

You can now use the mouse right-click to access quick Risk Simulator items in Excel, such as set assumptions, set forecasts, and run simulation.

In stochastic optimization, additional statistics are now available, including Percentiles as well as Conditional Means, such as obtaining the mean as long as it is > A or < A, which are critical in computing conditional value at risk measures.

The mean absolute deviation value is now changed to CV in the forecast chart’s statistics, where CV is the standard deviation divided by the mean, sometimes used as a proxy for volatility, and useful as a relative measure of risk, in comparing different sized projects, as well as being used as a risk-to-return ratio.

This new tool is used to compute various scenarios in your model, by changing one or two input variables at a time, for a range of inputs, to determine the effects on the output.

Additional checklists and options are now available, as are a more stable and powerful Tornado analysis (whereby you can now run Tornado across multiple worksheets), global settings (change one setting such as testing 10% upside and downside, and you can control if individual precedents are changed or the entire set of precedents are changed), and highlighting or ignoring possible integer values (sometimes integer values are used as flags in a model and this option helps in identifying potential precedents you may wish to ignore in running the Tornado). Worksheet names are now included in the sensitivity tables for easy identification, and other enhancements are included.

This optimization tool is capable of running multiple sets of optimizations with changing constraints. You can access this tool through the Set Constraints dialog box in optimization. This technique can be run in concurrence with static, dynamic and stochastic optimization.

This tool is now available on the Start | Programs | Real Options Valuation | Risk Simulator menu. It allows you to re-enable the software if Windows or Excel temporarily disables the software (this can occur if there is a power outage when you are running a simulation, if you get a virus or Trojan horse on your computer, if you accidentally delete some critical files, and so forth).

The module now is equipped with a Multiphasic Optimization and a test for Local versus Global Optimum in the “Advanced” options button (available when you run an optimization). These two new features, when used together with the existing advanced features, allow the user to have better control over how the optimization is run, and increases the accuracy and dependency of the results.

Select the data you wish to analyze, including the headers, and start this tool (located at Risk Simulator | Tools | Statistical Analysis), and the following analyses will be available:

- Descriptive statistics, including all 4 moments of the distribution as well as other confidence measures.
- Distributional fitting, to test if the data set can be fitted to any distributions.
- Hypothesis test to verify if the data are statistically significantly similar or different than a specific value.
- Nonlinear extrapolation to test if the data, a time-series, is nonlinear in nature.
- Normality test to see if the data set is statistically close to a normal distribution. This is an important statistical character because hypotheses tests as well as other modeling techniques require the normality assumption.
- Stochastic parameter estimations, to find the input parameters for a random walk, mean-reverting process, or jump-diffusion process, and to decide if the variations explained are sufficient to justify the use of the stochastic process forecast.
- Autocorrelation tests to see if the history of the time-series data can be used to predict the future.
- Trend analysis to test if the data set follows a linear time-trend and what the level of predictability is.
- Time-series forecasting, to test for the baseline shifts, trends, and seasonality effects of the time-series data.

Select the data you wish to analyze, including the headers, and start this tool (located at Risk Simulator | Tools | Diagnostic Tool), and the following: analyses are available:

- Heteroskedasticity.
- Multicollinearity.
- Micronumerosity.
- Nonlinearity.
- Outliers.
- Autocorrelation.
- Partial Autocorrelation.
- Distributive Lag.
- Normality and Sphericity.
- Nonstationarity.
- Stochastic Characteristics.
- Linear and Nonlinear Correlations.
- Variance Inflation Factors.
- Visual Charts.

These tests are vital before starting any types of forecasting or data analysis procedures. Each test comes complete with an easy-to-understand detailed report so that it does not take a trained econometrician or statistician to understand and interpret the results.

This is available at (Risk Simulator | Forecasting | Maximum Likelihood) whereby maximum likelihood iterative and internal optimization procedures are used to model binary response variables (the dependent variable is binary, taking on the values of 0 or 1). This is a key discriminant analysis with multiple uses (e.g., determining if patients will develop cancer given some characteristics such as age, cigarettes smoked, blood pressure; or to determine if a credit line or person will default on a loan given the company’s assets, asset volatility, or the person’s age, education level, years at a job, etc).

We have multiple language support, with English (USA), Chinese (Simplified), Spanish, and Japanese, with forthcoming editions with additional languages. Users can switch between languages midway while working on their models by simply clicking on the Risk Simulator and Languages menu, and restarting Excel.

We have completely upgraded our source code to work seamlessly with Microsoft .NET Framework 2.0/3.0. This translates to higher speed and compatibility with newer computers.

a. Dynamic Sensitivity Charts are now capable of taking both cell names and cell addresses.

b. Dynamic and Stochastic Optimization routines now support:

Conditional Means and Semi-Standard Deviations for CVar computations

c. Updated examples to showcase the use of Efficient Frontier analysis and multiple models econometrics

d. Updated Japanese user manual

e. Updated language edits and corrections in Spanish reports and Spanish user interface

f. Online resources menu inside Risk Simulator for quick and easy access at your fingertips

How do you make critical business decisions? Do you consider the risks of your projects and decisions, or are you more focused on returns? Do you have a hard time trying to understand what risk is, let alone quantifying risk? Well, our Risk Simulator software will help you identify, quantify, and value risk in your projects and decisions.

RISK SIMULATOR is a powerful Excel add-in software used for applying simulation, forecasting, statistical analysis, and optimization in your existing Excel spreadsheet models. The software was developed specifically to be extremely easy to use. For instance, running a risk simulation is as simple as 1-2-3, set an input, set an output, and run. Performing forecasting can be as simple as two or three mouse clicks away and the software does everything for you automatically, complete with detailed reports, powerful charts and numerical results. It even comes in English, Spanish, Chinese and Japanese, with additional languages on their way.

If we have the technology to send spacecrafts half way across the solar system, why can’t we spend a little more time quantifying risk? Such technology already exists and Risk Simulator encapsulates these advanced methodologies into a simple and user-friendly tool. We have books, live training (Certification in Risk Management) seminars, training DVDs, consultants and free sample getting started videos in risk analysis and modeling available on our website.

Risk Simulator is also integrated with our other software including the Real Options Super Lattice Solver, Employee Stock Options Valuation Toolkit, Modeling Toolkit (over 800 Functions and 300 Models), ROV Modeler, ROV Optimizer, ROV Valuator, ROV Basel II Modeler, ROV Compiler, ROV Extractor and Evaluator, and ROV Dashboard. Please visit our website for more details.

- 10 books on risk analysis, simulation, forecasting, optimization, real options, and options valuation written by the software’s creator
- Training DVDs on risk analysis (simulation, forecasting, optimization, real options, and applied business statistics)
- Live training and certification courses on general risk management, risk simulation, forecasting, optimization, and strategic real options analysis
- Detailed user manual, help file, and an extensive library of example files
- Live project consultants with advanced degrees and years of consulting and industry experience

Risk Simulator can be downloaded immediately from our website with a default 10 day trial license. Our philosophy is you get to try before you buy. Once you use it, we are convinced you will fall in love with the simplicity and the power of the tool, and it will become an indispensible part of your modeling toolbox. We also have academic licenses for full time professors teaching risk analysis (and their students) or other associated courses using Risk Simulator or our other software products. Contact admin@realoptionsvaluation.com for details.

Advanced analytical tools such as the Risk Simulator software are built to be easy to use but may get the analyst in trouble if used inappropriately. Sufficient theoretical understanding coupled with pragmatic application experience is vital; therefore, training is critical.

Our Risk Analysis course is a two-day seminar focused on hands-on computer-based software training, with topics covering the basics of risk and uncertainty, using Monte Carlo simulation (pitfalls and due diligence), and all of the detailed methods in forecasting and optimization.

We also have a Real Options for Analysts course for the analysts who want to immediately begin applying strategic real options in their work, but lack the hands-on experience with real options analytics and modeling. This two-day course covers how to set up real options models, apply real options, and solve real options problems using simulation, closed-form mathematics, and binomial and multinomial lattices using the Real Options SLS software.

The Certified Quantitative Risk Management (CQRM) seminar is a four-day hands-on class that covers the materials on our Risk Analysis and Real Options for Analysts courses and geared towards the CQRM certification provided by the International Institute of Professional Education and Research (AACSB member and eligible for 30 PDU credits with the Project Management Institute, Inc (PMI)^{®}).

Our Risk Analysis for Senior Managers is a one day course specially designed for senior executives, where we will review case studies in risk management from 3M, Airbus, Boeing, GE, and many others. It provides an executive overview of risk analysis, strategic real options, portfolio optimization, forecasting and risk concepts without the technical details.

Also available are other customized decision, valuation and risk analysis courses with an emphasis on on-site trainings customized to your firm’s exact needs based on your business cases and models). Consulting services are available, including the framing of risk analysis problems, simulation, forecasting, real options, risk analytics, model building, decision analysis, integrated OEM and software customization.

Dr. Johnathan Mun is the software’s creator and teaches the Risk Analysis, Real Options for Analysts, Risk Analysis for Managers, CRM, and other courses. He has consulted for many Fortune 500 firms (from 3M, Airbus, and Boeing to GE and Motorola) and the government (Department of Defense, State and Federal Agencies) on risk analysis, valuation, and real options, and has written a number of books on the topic, including Real Options Analysis: Tools and Techniques, 1st and 2nd Edition (Wiley Finance, 2002, 2005); Real Options Analysis Course: Business Cases (Wiley Finance, 2003); Applied Risk Analysis: Moving Beyond Uncertainty in Business (Wiley, 2003); Valuing Employee Stock Options Under 2004 FAS 123 (Wiley Finance, 2004); Modeling Risk: Applying Monte Carlo Simulation, Real Options Analysis, Forecasting and Optimization (Wiley, 2006); Advanced Analytical Models: 800 Functions and 300 Models from Basel II to Wall Street and Beyond (Wiley 2008); The Banker’s Handbook on Credit Risk: Implementing Basel II (Elsevier Academic Press 2008); and others. He is the founder and CEO of Real Options Valuation, Inc., and is responsible for the development of analytical software products, consulting, and training services. He was formerly Vice President of Analytics at Decisioneering, Inc. (Oracle), and was a Consulting Manager in KPMG’s Global Financial Strategies practice. Before KPMG, he was head of financial forecasting for Viking, Inc. (an FDX/FedEx Company). Dr. Mun is also a full professor at the U.S. Naval Postgraduate School, a professor at the University of Applied Sciences and Swiss School of Management (Zurich and Frankfurt), and has held other adjunct professorships at various universities. He has a Ph.D. in finance and economics, an MBA in business administration, an M.S. in the area of management science, and a BS in applied sciences. He is certified in Financial Risk Management (FRM), Certified in Financial Consulting (CFC), and Certified in Risk Management (CRM).

PMI is a registered mark of the Project Management Institute, Inc.