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.
Resources
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.
A set of examples files to illustrate real options of projects:
1. A model that illustrates the real option of abandoning a project at a future time if the project is not doing well.
2. A model that illustrates the real option of postponing investment in a project rather than investing right away.
3. A model that illustrates the real option of expanding a project at a later date.
4. A model that illustrates the real option of reducing the size of a project at a later date.
5. A model that illustrates the real option of participating in a "pioneer" project, a money-loser that allows a company to "get in the game".
6. A model that illustrates a sequence of two real options in the pharmaceutical industry.
A model that illustrates how the Time Series Fit feature can be used on a series with trend and seasonality.
