Comparing Simulation Performance for Crystal Ball, R, Julia and @RISK

The Need For Speed 2019
The Need for Speed 2019 study compares Excel Add-in based modeling using @RISK and Crystal Ball to programming environments such as R and Julia. All 3 aspects of speed are covered [time-to-solution, time-to-answer and processing speed] in addition to accuracy and precision.
  • 25 February 2019
  • Author: Eric Torkia
  • Number of views: 260
  • Comments: 0

How I Learned to Think of Business as a Scientific Experiment

Bayesian Reasoning using R (Part 2) : Discrete Inference with Sequential Data
Imagine playing a game in which someone asks you to infer the number of sides of a polyhedron die based on the face numbers that show up in repeated throws of the die. The only information you are given beforehand is that the actual die will be selected from a set of seven die having these number of faces: (4, 6, 8, 10, 12, 15, 18). Assuming you can trust the person who reports the outcome on each throw, after how many rolls of the die wil you be willing to specify which die was chosen?
  • 6 November 2018
  • Author: Robert Brown
  • Number of views: 612
  • Comments: 0
Modeling Time-Series Forecasts with @RISK

Making decisions for the future is becoming harder and harder because of the ever increasing sources and rate of uncertainty that can impact the final outcome of a project or investment. Several tools have proven instrumental in assisting managers and decision makers tackle this: Time Series Forecasting, Judgmental Forecasting and Simulation.

This webinar is going to present these approaches and how they can be combined to improve both tactical and strategic decision making. We will also cover the role of analytics in the organization and how it has evolved over time to give participants strategies to mobilize analytics talent within the firm.

We will discuss these topics as well as present practical models and applications using @RISK.

  • 18 October 2011
  • Author: Eric Torkia
  • Number of views: 622
  • Comments: 0
The Need for Speed: A performance comparison of Crystal Ball, ModelRisk, @RISK and Risk Solver

Need for SpeedA detailed comparison of the top Monte-Carlo Simulation Tools for Microsoft Excel

There are very few performance comparisons available when considering the acquisition of an Excel-based Monte Carlo solution. It is with this in mind and a bit of intellectual curiosity that we decided to evaluate Oracle Crystal Ball, Palisade @Risk, Vose ModelRisk and Frontline Risk Solver in terms of speed, accuracy and precision. We ran over 20 individual tests and 64 million trials to prepare comprehensive comparison of the top Monte-Carlo Tools.

  • 20 September 2011
  • Author: Eric Torkia
  • Number of views: 598
  • Comments: 0
Excel Simulation Show-Down Part 3: Correlating Distributions

Escel Simulation Showdown Part 3: Correlating DistributionsModeling in Excel or with any other tool for that matter is defined as the visual and/or mathematical representation of a set of relationships. Correlation is about defining the strength of a relationship. Between a model and correlation analysis, we are able to come much closer in replicating the true behavior and potential outcomes of the problem / question we are analyzing. Correlation is the bread and butter of any serious analyst seeking to analyze risk or gain insight into the future.

Given that correlation has such a big impact on the answers and analysis we are conducting, it therefore makes a lot of sense to cover how to apply correlation in the various simulation tools. Correlation is also a key tenement of time series forecasting…but that is another story.

In this article, we are going to build a simple correlated returns model using our usual suspects (Oracle Crystal Ball, Palisade @RISK , Vose ModelRisk and RiskSolver). The objective of the correlated returns model is to take into account the relationship (correlation) of how the selected asset classes move together. Does asset B go up or down when asset A goes up – and by how much? At the end of the day, correlating variables ensures your model will behave correctly and within the realm of the possible.

  • 19 August 2011
  • Author: Eric Torkia
  • Number of views: 928
  • Comments: 0