Risk Simulator

Risk Simulator is a powerful Excel add-in software and is used for running multiple scenarios and simulating uncertainties for the purposes of forecasting and risk analysis. We pride ourselves on a simple easy-to-use interface backed by highly sophisticated computational and simulation engines. This software is also used for forecasting time-series and cross-sectional data, as well as for portfolio optimisation.

Monte Carlo Simulation (runs parametric and nonparametric simulation of 24 probability distributions with different simulation profiles, truncated and correlated simulations, customisable distributions, precision and error controlled simulations, and many other algorithms)
Forecasting (runs Box-Jenkins ARIMA, multiple regression, nonlinear extrapolation, stochastic processes, and time-series analysis)
Optimisation Under Uncertainty (runs optimisations using discrete integer and continuous variables for portfolio and project optimisation with and without simulation)
Modelling and Analytical Tools (runs tornado, spider, and sensitivity analysis, as well as bootstrap simulation, hypothesis testing, distributional fitting, etc.)
Automatic reports

Together with SLS Software, the tools can help you identify, quantify, forecast, value, diversify, and hedge risks. Use these advanced analytic tools to maximise your profits, drive your revenues, reduce your costs, cut down on development time, and increase product and service quality. Instead of relying on guesses in traditional modelling and decision analysis, push the envelope and take your decision analysis to the next level. Use these tools to gain valuable insights into your decisions, create the risk and return profiles of your projects, and quantify ways to mitigate your risks. Using your existing Excel models, add these risk analysis technologies to identify your project’s or asset’s critical success factors, to predict and quantify risks, and to define strategies to mitigate these risks.

24 statistical distributions and one customisable empirical nonparametric distribution
Complete integration with Excel (dynamic linking, VBA macros, and others)
Comprehensive simulation and analytical reports for each functionality
Correlated simulation with distributional truncation
Multidimensional simulations with uncertain input parameters
Simulation profiling for scenario analysis in simulation
Box-Jenkins ARIMA models (time-series forecasting)
Multiple regression analysis (time-series, cross-sectional and panel modelling and forecasting)
Nonlinear extrapolation (time-series forecasting)
Stochastic process forecasting (time-series forecasting without data)
Time-series analysis forecasting (time-series forecasting)
Optimisation with continuous variables
Optimisation with discrete integer variables
Optimisation with mixed continuous and discrete variables
Linear optimisation
Nonlinear optimisation
Static optimisation (fast and provides single-point estimates)
Dynamic optimisation (simulation with optimisation)
Stochastic optimisation (multiple iterations with distributions of decision variables)
Distributional fitting of existing data
Hypothesis testing of distributions
Nonparametric bootstrap simulation
Sensitivity analysis
Tornado and spider charts
A user-friendly interface
A comprehensive User Manual, illustrating each module's functionality with case examples and applications
Short Strategic Business Cases illustrating real-life applications of risk analysis, simulation, forecasting, optimisation, and real options, starting with the framing of the problem through to its software solution

Advanced analytical tools such as the Risk Simulator and SLS software might 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. The Risk Analysis course is a two-day seminar focused on hands-on software training. Topics covered include the basics of risk and uncertainty, using Monte Carlo simulation (pitfalls and due diligence), truncation, correlated simulations, statistics for interpreting the results, distributional fitting, bootstrap simulation, sensitivity analysis, forecasting (time-series and cross-sectional forecasting), extrapolation, stochastic process forecasting, linear optimisation (integer and continuous optimisation), and many more exciting topics.

Other courses are also offered, including Real Options for Analysts for the analysts who want to begin applying real options in their work, but lack the hands-on experience with real options analytics and modelling. 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 lattices. It focuses on detailed case studies and practice valuation models using the Real Options SLS software. Also available are other risk analysis courses with an emphasis on customised on-site trainings (simulation, forecasting, optimisation, and real options trainings customised to your firm’s exact needs based on your business cases). Consulting services are also available, including the framing of risk analysis problems, simulation, forecasting, real options, risk analytics, model building, decision analysis, and software customisation.

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Melbourne, Australia
(Sat - Thursday)
(10am - 05 pm)