VCU Students use @RISK to Analyze Amazon and FedEx Financial Risk
Dr. Etti Baranoff, an Associate Professor of Insurance and Finance at Virginia Commonwealth University in Richmond, has her students collect real-life financial info about current companies so they can learn how to apply Value at Risk (VaR) analysis. Dr. Baranoff's students use @RISK to analyze which accounting data inputs (from both the balance sheet and income statements) can be most damaging to the net income and net worth of a company.
Last year, students constructed in-depth case studies Amazon, FedEx, and several other large companies. For each study, students use @RISK to assign distributions to data, assess Value at Risk for the company's net worth with correlation among the inputs, perform sensitivity analyses and stress tests, construct risk maps, and more. A few sample graphs are shown below.
Figure 1 - Value at Risk (VaR) for Amazon net-worth with correlation among the inputs
Figure 2 - Stress Test for Amazon net worth with correction among the inputs
By the end of the course, the data is assembled for each company and compared, and students have the tools to act as a risk committee to debate the risks and ways to mitigate them.
A comparison of all case studies for the Fall 2014 semester of the Managing Financial Risk course
For Dr. Baranoff, @RISK’s ease of use is a major reason for making it the tool of choice for her classroom. She highlights its ability to provide credible statistical distributions as another major plus. “@RISK allows me to show students the differences of selecting different statistical distributions, and the importance of correlation among some of the inputs,” she says. “Additionally, it allows us to combine the results for VaR analysis, stress analysis, and sensitivity analysis to discover what inputs can be destructive to a company’s net income and net worth."
"At the end of the day," she continues, "it gives us good viewpoints to compare the results among the cases. The stories and the results lead to debate, and provide lots of fun in the classroom as the whole class become a risk committee.”
» Read the full case study
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NCAA March Madness– Simulate this
Year's Tournament with @RISK
Excerpted from Monday's webcast presented by Andrew Pulvermacher of Nighthawk Intelligence.
In a recent webinar, Andrew Pulvermacher of Nighthawk Intelligence demonstrates an @RISK model that could help you dominate your 2015 NCAA tournament office pool!
According to ESPN Magazine, the NCAA tournament is one of the most wagered-on events in sports. Veteran bookmakers estimate bets range from $12 billion to $26 billion (roughly the GDP of Honduras). If you’re looking for that edge to improve your office pool odds, take a look at the webcast and open up the accompanying @RISK model. Using publically available data, you’ll be able to tame March Madness by managing uncertainty.
» Simulating the 2015 NCAA tournament with @RISK
» 2015 NCAA tournament model using @RISK
» Pulvermacher's PPT from the webinar
The DecisionTools Suite is a key component in major textbooks in business, OR, and engineering.
The Economic Analysis of Industrial Projects, 3rd Ed.
Ted G. Eschenbach, Neal A. Lewis, Joseph C. Hartman, and Lynn E. Bussey
Economic Analysis of Industrial Projects, Third Edition, provides the best possible methods for applying economic analysis theory to practice. Completely revised and expanded in this new edition, the text now includes five new chapters and new material on real options analysis and replacement analysis. This advanced engineering economics textbook uses @RISK and PrecisionTree extensively in Chapters 13 and 14 where they cover 'Decision Making Under Risk' and 'Real Options Analysis'.
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The DecisionTools Suite is part of complex analyses published in white papers.
A Targeted Health Risk Assessment Following the Deepwater Horizon Oil Spill: Polycyclic Aromatic Hydrocarbon Exposure in Vietnamese-American Shrimp Consumers
Mark J. Wilson, Scott Frickel, Daniel Nguyen, Tap Bui, Stephen Echsner, Bridget R. Simon, Jessi L. Howard, Kent Miller and Jeffrey K. Wickliffe; 'Environmental Health Perspectives'; Volume 123 - Issue 2 - February 2015
The Deepwater Horizon oil spill of 2010 prompted concern about health risks among seafood consumers exposed to polycyclic aromatic hydrocarbons (PAHs) via consumption of contaminated seafood. The objective of this study was to conduct population-specific probabilistic health risk assessments based on consumption of locally harvested white shrimp (Litopenaeus setiferus) among Vietnamese Americans in southeast Louisiana. The authors used @RISK to conduct Monte Carlo simulations, along with distribution fitting, fit testing, probabilistic risk assessments, and sensitivity analyses.
Read the full paper here
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