Resource Library

Anybody who owns a portfolio of investments knows there is a great deal of uncertainty about the future worth of the portfolio. Recently the concept of value at risk (VAR) has been used to help describe a portfolio's uncertainty. Simply stated, value at risk of a portfolio at a future point in time is usually considered to be the fifth percentile of the loss in the portfolio's value at that point in time. The following example shows how @RISK can be used to measure VAR. The example also demonstrates how buying puts can greatly reduce the risk in a stock.
This example was taken from Chapter 62 of Financial Models using Simulation and Optimization by Wayne Winston, published by Palisade Corporation, where a detailed, step-by-step explanation can be found.

This model, really a template, is used to value European stock options: calls and/or puts. The model uses the well-known lognormal model of stock price changes to simulate the future stock price. This formula depends on the time till expiration, the riskfree rate, and the volatility of the stock, as well as a standard normal variate.

A Markov chain is a process observed through time where the probability distribution of the next state of the process, given the current state, is independent of the past states. This model illustrates the evolution of prices through time.

The purpose of this model is to provide a comparison between building an onshore plant in the U.S. and building an offshore plant in China. The model is for a company based in the U.S. with sales in the U.S. However, despite transportation costs, there might be benefits to building in China. The model includes uncertainty in the exchange rate, weekly demand, and amounts of extra weekly capacity available. The future exchange rates are based on fitting historical exchange rates with @RISK's Time Series Fit tool.

A new product model that illustrates @RISK's Advanced Sensitivity Analysis feature.

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.