Resource Library

A set of example files which shows you how to use the more useful features of @RISK to a basic model.
1. A deterministic model to get started.
2. A basic @RISK model with uncertain revenue and cost.
3. A version where revenue and cost are not only uncertain but correlated.
4. A version that illustrates the RiskSimtable function for examining the effect of different standard deviations of revenue and cost.
5. A version that illustrates @RISK's Goal Seek feature for forcing the standard deviation of profit to a specified value.
6. A version that illustrates @RISK's Stress Analysis feature for examining conditional (usually tail) distributions of profit.
7. A version that illustrates @RISK's Advanced Sensitivity Analysis feature for checking how sensitive an output is to various inputs.
8. A version that checks whether the forms of the input distributions have much effect on the output distribution.

Two versions of a basic @RISK model where:
1. Only one competitor can enter the market.
2. Multiple competitors can enter or exit the market.

A basic @RISK model where:
1. The uncertainty is essentially stationary through time.
2. The uncertainty changes through time because of business cycles.
3. The lender's price and sales volume are correlated each year.

A set of basic @RISK model where you can find:
1. A deterministic model to get started.
2. Various quantities are uncertain but don't change from year to year.
3. Various uncertain quantities are allowed to change from year to year.
4. Revenue growth rates not only change from year to year but are correlated.
Click here to see a video of this example.

A model that illustrates options for hedging against exchange rate variability, using the Time Series Fit feature on historical exchange rate data.

A model which illustrates how to run an Excel Goal Seek each iteration of a simulation.

A model for projecting future financial values, with various sources of uncertainty.

A portfolio model that uses the Time Series Batch Fit feature to project future stock prices from historical stock prices.

A basic portfolio model with correlated assets, including a comparison across different correlation values.

When a new product is developed, it is important to forecast its future financial path. Here you will find a set of examples to do it:
1. A deterministic model to get started.
2. A version using TopRank to see which of many inputs are important enough to model with uncertainty.
3. A basic @RISK model where the most important inputs, based on the TopRank analysis, are modeled with uncertainty.
4. A version with somewhat less uncertainty in the inputs, used for comparison with the previous version.

An example where extremes in a portfolio of stock options are compared using copulas versus correlations.

If you own a portfolio of investments, you know that there is a great deal of uncertainty about the future worth of the portfolio. The concept of value at risk (VAR) can be used to help describe a portfolio's uncertainty. Here, you will find a set of examples to model this type of problem:
1. A deterministic model to get started.
2. A basic @RISK model where future stock prices are independent of one another.
3. A version where the investor can purchase put options on the stocks held to mitigate risk.
4. Another version with puts that illustrates @RISK's Sensitivity Analysis features.
5. A version that illustrates @RISK's Goal Seek feature for forcing the mean return from a stock to a specified value.
6. A version where future stock prices are correlated with one another.
7. A version that determines the puts that result in an optimal portfolio of stocks and puts.
8. A model for finding a sequence of optimal portfolios for a sequence of expected levels of return.
Click here to see a video of this example.