@RISK Modeling Tips
Helicopter Manufacturer AgustaWestland Uses @RISK to Inform Major Company Decisions
AgustaWestland first builds a ‘deterministic’ Excel model to predict key financial outputs such as revenue, net profit, Net Present Value (NPV), Internal Rate of Return (IRR) and financial break-even. Inputs to the model include: the cost of engineering studies for the design and development of the new product; prototype manufacture; flight tests; certification; etc. Financial parameters such as inflation and exchange rates in different currencies are also accounted for.
AgustaWestland then carries out an @RISK analysis using Monte Carlo simulation to determine the accuracy of the forecasts and the way to improve the business, both in true feasibility and in financial results. The models must take uncertainty into account because the economic situation cannot be predicted with any great accuracy, especially when the business cases are based on a period of 20 years. Using @RISK graphs, AgustaWestland can see which inputs have the greatest effect on the financial outputs, and therefore require attention.
“Our use of the risk analysis element of Palisade’s DecisionTools Suite has enhanced our ability to assess, control and drive company decisions. We can now focus on the key activities that enable us to pursue the best product within the most appropriate financial timeframe,” says Francesca Schiezzari, a Senior Analyst at AgustaWestland who uses @RISK to build similar financial business cases for a variety of company projects.
2014 Palisade Regional Risk Conferences
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Save the date for Palisade's premiere 2-day Risk Conference to be held in New Orleans at the Hilton New Orleans Riverside Hotel. This is a must-attend event, with four tracks of informative sessions that will give attendees opportunities to learn, network, and explore the broad range of applications our software provides.
Palisade's World Cup Model
This model grabbed the interest of both mainstream and industry publications; the Wall Street Journal featured the forecast in their article “The Journal’s Prediction”. It reads, “World Cup prediction algorithms are notoriously predictable. And they're often flat wrong…So for 2014 prediction, the Journal looked to Fernando Hernandez, a consultant and trainer at Palisade Corp…[who] created a brilliantly logical prediction model…”
The model was also featured in the Risk Management Monitor, the official blog of Risk Management magazine that provides daily articles, commentary, interviews, podcasts and videos related to the world of risk management and insurance. The piece, written by Palisade Vice President Randy Heffernan, notes how the model can be used to make more exact predictions on the world-famous tournament. “Many will be putting money on the various matches—basing their bets on national pride or gut feelings,” Heffernan writes. “There is another option, however. If you have the data and the inclination, you could also utilize a Monte Carlo simulation to place your wager.”
Berlin used Palisade software to find an optimal portfolio sweet spot that avoids exorbitant risk while yielding satisfactory returns. Berlin simulated changes in capital markets assumptions and adjusted a mix of hedges to identify an optimal portfolio. The software helped prove to his clients that quantitative measures of simulation could help identify an optimal portfolio, rather than the typical qualitative approach. “RISKOptimizer and @RISK shuffle the deck a million times, and let you see how all those different combinations could play out,” he says.
In Rosenberg’s case, he used @RISK to analyze commercial real estate investments. “There are a lot of variables that are highly uncertain,” he explains. These include rent rates, lease renewal probabilities, interest rate changes, etc. Because all these variables could combine in a myriad of ways, Rosenberg used @RISK to illustrate the range of outcomes to his client. “Our models use @RISK to ultimately give us a dispersion of values and a risk assessment of the property rather than one single number of what the investment is worth.”
@RISK and the DecisionTools Suite have applications in a broad range of sectors, particularly in the oil and gas industry. Those who are interested in analyzing and modeling oil prices can learn a great deal from this section of Roy Nersesian's Energy Risk Modeling book. The excerpt gives a detailed primer on the history of oil pricing and production control—from the days of Rockefeller’s benevolent monopoly to Saddam Hussein’s rumored oil price manipulations--then takes the reader through step-by-step guides on how to best use Palisade software to analyze historical oil price data and model future prices. A must-read for anyone wanting a better understanding of this vital commodity and how best to anticipate its future fluctuations.
Análise Quantitativa de Risco Utilizando
Introduction to Risk and Decision Analysis
Business Forecasting and Simulation using
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