The Casualty Actuarial Society (CAS) and the Society of Actuaries (SOA) commissioned three prominent authorities in finance, actuarial science, and mathematics to develop a model using @RISK for pension and insurance risk planning. Kevin Ahlgrim, Illinois State
University, and Stephen D'Arcy and
Richard Gorvett, both of University of
Illinois Champaign-Urbana,
collaborated on the research and
the model development.
Seminar Schedule
:: Regional Seminars
Risk and Decision Assessment Training using @RISK and the DecisionTools Suite
July 10–11, Seattle, WA
July 24–25, Montreal, QB
Project Risk Assessment
using @RISK for Project
July 31–Aug 1, Ithaca, NY
:: Live Web Training
Risk and Decision Assessment Using @RISK: Part I
June 28–29
July 17–18
Risk and Decision Assessment Using @RISK: Part II
July 20–21
Expert Answers to
Technical Questions
Dear Amy,
Why is there a difference between the expected value that appears in the spreadsheet cell that contains an @RISK function and the mean of the simulation results for the input?
— A.S.
Dear A.S.,
This is normal statistical behavior, and is really the difference between a theoretical value and an observed value.
To illustrate, set up a simulation in the following way:
Industry Application: Hedged Cash Flow Art Misyan, Director of Foreign Exchange at Merck, is responsible for the company's ongoing surveillance of its currency risks. He has been using @RISK for years. "We love it because it incorporates BestFit and gives us the flexibility to evaluate alternative distributions on screen." "Simple VaR analysis is not good enough," he says. "We are managing currency risk in both the balance sheet and in future revenues ... while simulating hedged currency risks on the balance sheet is relatively straightforward", Misyan states, "simulating hedged cash flow currency risk is not”. Earlier methods of evaluating hedging strategies predicted economic value at inception and completion. Now, current practice is to project both economic and accounting hedge performance through time, looking prospectively and retrospectively. @RISK is well-suited to handling the task of significant increases in the number of variables for assessing VaR in exchange rates. read more about Merck's application of @RISK for currency exchange analysis @RISK was used by the management consultancy HVR Consulting Services, Ltd. to model the uncertainty surrounding the furnace shutdown duration. The simulation proved key to meeting the planned shutdown period, focusing management attention on potential trouble spots and highlighting opportunities to save on downtime. The consultants also modeled all the uncertainties surrounding the Corus business in a business process model. @RISK was again used, enabling production and shipment at all east-coast Corus sites to be simulated incorporating the effects of the identified business risks. This model guided the Corus steering committee in its decisions on production and sales commitments. As a result of the Corus management acting upon the results of the simulations the project was completed within the allowed shutdown period, without depleting the stockpiled steel to a dangerous level. The Corus success was the result of forward-looking managers using a forward-looking tool, @RISK. read the full article Steel Giant Uses @RISK to Mitigate Losses During Furnace Shutdown :: PrecisionTree Features: Advanced Features PrecisionTree Pro, available in both the Standard and Professional versions, has a number of additional features to provide you more advanced Decision Analysis capabilities, including Policy Suggestion Report and Strategy Region Graphs. read more influential case studies involving PrecisionTree
Value-at-Risk with Exchange Rates
Conducting business across national - and often international - borders increases risk management and decision making complexity enormously. Just determining Value-at-Risk (VaR) for exchange rates impacts numerous areas of commerce including distribution channels, investments, and facility siting to name a few.
For multinational pharmaceutical giant Merck, with currency exposures spanning at least 30 countries, awareness of value-at-risk (VaR) is crucial to the performance of its risk management programs. 
read more case studies
learn more about @RISK
Case Study:
Steel Giant Uses @RISK to Mitigate Losses
During Furnace Shutdown
In any manufacturing industry major repairs are inevitable, but the costs involved and the impact on the business are difficult to predict. In planning a large-scale repair to a principal iron-making plant the steel giant Corus was faced with significant downtime, productivity loss and revenue shortfalls. @RISK helped them minimize these risks and resume normal operations ahead of schedule.
learn more about @RISK
Product Spotlight:
PrecisionTree
:: Decision Analysis in Excel
Decisions, decisions, decisions! When you are faced with large, complex, and sequential decisions, you need to organize them efficiently while considering all possible options. Decision trees and influence diagrams are perfect for this, and PrecisionTree lets you create them right where you work the most - in Excel. With PrecisionTree you can easily create diagrams by selecting cells and clicking node buttons at the PrecisionTree toolbar; you can enter probabilities and payoffs directly in cells in your tree; and with one click, PrecisionTree will run a powerful decision analysis on your model, determining the best way to proceed with your decision.
Businesses use PrecisionTree when introducing new products, factoring in decisions at each stage of marketing and production. Gas and oil companies use it to make the best decisions when developing an oil field. 
PrecisionTree includes many advanced analysis options including Sensitivity Analysis, Chance Nodes, Decision Nodes, End Nodes, Logic Nodes, Reference Nodes, Custom Utility Functions, Linked Trees, Risk Analysis on Your Decision Trees and risk analysis on your decision trees.
learn more about PrecisionTree
Actuarial Societies Endorse an @RISK Model continued from above
The Research Project
The team first investigated known relationships among economic variables, particularly relating to interest rates, inflation and equity returns, and provided summaries of that information on the societies’ web sites. Next they surveyed the state of the art of modeling in the insurance industry to identify best practices and ways in which they might advance those practices.
The Financial Scenario Model
The resulting model provides an integrated framework for sampling future financial scenarios that represent a reasonable approximation of historical values (see graph on the full article). @RISK’s built-in 
probability distribution functions, correlation matrices, and simulation results were essential to the study because they allowed the model to correlate such variables as the performance of stocks and bonds, the housing market, and natural disasters with interest rates, inflation, and unemployment. Capturing the interplay among these variables creates a far more accurate model, and is proving useful for dynamic financial analysis (DFA), dynamic financial condition analysis, pricing embedded options in insurance contracts, solvency testing, and operational planning, among other applications.
D’Arcy says the insurance industry benefits since “unforeseen events can create havoc with insurance rates, and better modeling tools will result in better prepared insurance companies and more consistent pricing for insurance buyers.”
D’Arcy continues stating that, “Many actuarial firms have developed their own, proprietary DFA models. The difference between the existing models and the one we developed for the societies is that our model is publicly accessible.” The report, supporting presentations, and model are available from the Casualty Actuary Society website or the Society of Actuaries website. The model is free and can be used by any interested party.
Saving Time and Resources with @RISK
Ahlgrim stated, “From our perspective, @RISK saved time and resources, especially during the model’s development. The interface through Excel allowed us to use a tool with which actuaries are very comfortable, and introduce stochastic modeling procedures without resorting to programming. Writing code to simulate financial processes, incorporate correlations, and capture output would have been a formidable task. @RISK allowed us to develop these features quickly and easily.”
read the full article Actuarial Societies Offer @RISK Model
learn more about @RISK
Ask Amy continued from above
Start with a blank workbook. In cell A1, define an @RISK input with a normal distribution that has a mean of 10 and a standard deviation of 1.
Select the Simulation Settings icon from the @RISK toolbar. On the Iterations tab, set the number of iterations to 10,000. On the Sampling tab, select Latin Hypercube for the sampling type and a fixed random generator seed of 1.
Run the simulation and take a look at the @RISK - Results window. Statistical theory tells us that the expected distribution for the input is a normal distribution with a mean of 10 and a standard deviation (often called the "Standard Error") = 1/sqrt(10000) or .01. The mean for the input in the '@RISK - Results' window is 9.999953 which is well below the standard error.
E-mail comments or suggestions to: sales@palisade.com
©2006 Palisade Corporation, 798 Cascadilla Street, Ithaca, NY 14850 USA
$unsub