Simulating the Financial Consequences of the Subprime Mortgage Crisis
by Roy Nersesian, September 22, 2008
Roy Nersesian describes how the selling of mortgages as investments (collateralized mortgage obligations, or CMOs), coupled with lax governmental regulation and the greed of house flippers fueled the flames of the home buying and building frenzy. He uses an @RISK simulation to show exactly how and where the investors lost in the ensuing housing market meltdown.

Risk Analysis and Contingency
Determination Using Range Estimating
by Dr. Kenneth K. Humphreys, PE CCE; AACE International Recommended Practice No. 41R-08, June 25, 2008
This Recommended Practice (RP) of AACE International describes the process known as range estimating, a methodology to determine the probability of a cost overrun (or profit underrun) for any level of estimate and determine the required contingency needed in the estimate to achieve any desired level of confidence. The process uses range estimating and Monte Carlo analysis techniques. The RP provides the necessary guidelines for properly applying range estimating and Monte Carlo analysis to determine probabilities and contingency in a reliable manner using risk analysis software packages.

The Use of Video Tutorials in a Mathematical Modeling Course
by Aimee J. Ellington and Jill R. Hardin, redOrbit, 28 May 2008
In Mathematical Modeling, a semester-long course at Virginia Commonwealth University, students are exposed to the role that technology plays in modern applications of mathematical models. PrecisionTree and @RISK, which "represent the state of the art in solution methodology," are used in the course. This paper describes the modeling course, the video tutorials, and an assessment of the usefulness of the tutorials.

» View the tutorials designed for the course

Project Cost Risk Analysis: The Risk Driver Approach Prioritizing Project Risks and Evaluating Risk Responses
By David T. Hulett, Keith Hornbacher, and Waylon T. Whitehead; Hulett Associates, LLC, 2008
This presentation outlines limitations of the traditional three-point estimate approach to quantitative risk analysis.
The authors introduce the Risk Driver method to cost risk analysis in projects. The approach uses @RISK, and a
sample spreadsheet model with risk registers is included so you can try it yourself. A simple refinery construction
example is included.

Download sample spreadsheet model

Actuaries Excel: But What About Their Software?
Louise Pryor et al, General Insurance Convention, 26 September 2006
Independent survey of actuaries across North America and Europe revealed that @RISK is the third most widely used software by actuaries, after Microsoft Office and in-house reserves software.

@RISK and BestFit Models Provide Insight on
Biological Control Impact on Monarch Butterflies.
R. L. Koch et al pub: Biological Invasion, 2006
A quantitative risk assessment Study from the University of Minnesota.

Practical Calculation of Expected and Unexpected
Losses in Operational Risk by Simulation Methods
Enrique Navarrete, Scalar Consulting, 2006
This paper surveys the main difficulties involved with the quantitative measurement of operational risk and proposes simulation methods as a practical solution for obtaining the aggregate loss distribution. An example that calculates both expected and unexpected losses as well as operational risk VAR is provided.

Fast and Robust Monte Carlo CDO Sensitivities and
their Efficient Object Oriented Implementation
Marius Rott and Christian Fries, DefaultRisk.com, May 31, 2005
In this paper we present a simple yet generic method for fast and robust Monte-Carlo calculation of sensitivities of Collateralized Debt Obligations (CDOs).

Application of a Monte Carlo Simulation Method for
Predicting Voltage Regulation on Low-voltage Networks
Power Systems IEEE Transactions, February 2005, Vol 20, Issue 1
Estimating voltage regulation on Low-Voltage (LV) networks is "bread and butter" work for many electricity network engineers. This paper summarizes research work on Monte Carlo modeling of residential electricity demand and its application to LV regulation problems.

Schedule Risk Analysis Simplified
David Hulett, Hulett and Associates, 2004
This slide show was presented by David Hulett of Hulett and Associates at the Project Management Institute annual meeting. It is a brief overview showing the benefits of accounting for uncertainty in your project models.

When CAPM Is Not Reliable Anymore, Simulation
and Optimization Tools Will Get the Job Done
Guilherme Marques Calôba, UFRJ, Regis da Rocha Motta, UFRJ, Alfredo Julio Souza Prates, Petrobrás, Marcelo Marinho Simas, Petrobrás, 2004
The authors compare portfolio optimization by two distinct methodologies. The first is the usual Capital Asset Pricing Model (CAPM) efficiency frontier, which considers that returns from every asset are normally distributed. Many apply this type of optimization to assets whose return distribution is simply non-normal.

Health and Economic Impact of Posttranfusion Hepatitis B
Arturo Pereira, Transfustion, Vol 43, February 2003
@RISK is used to evaluate the effectiveness of new HBsAG assays in donor testing in order to reduce the risk of posttransfusion hepatitis B tranmission.

» Read summary case study.

The Economics of Litigation
Samson Vermont, 2001
This five-part series of articles outlines how decision trees and risk analysis techniques can be applied to a patent litigation case.

Combining Preference Theory and CAPM Efficient Frontier: Towards and Optimum Portfolio of Upstream Projects
SEG/San Antonio 2001 Expanded Abstracts, 2001
Regis da Rocha Motta et. al.

Investment and Risk Analysis
Applied to the Petroleum Industry
Regis da Rocha Motta et. al.

Estimating the Cost of Preventive Services in Mental
Health and Substance Abuse Under Managed Care
Substance Abuse and Mental Health Administration, Department
of Health and Human Services, December 2001