Scenario analysis examples are computer-based applications used for exploring and simulating complex business and economic scenarios. They can be used to study how different economic policies affect the real world. For example, you can use Dragon to simulate forex market ups and downs. You don’t have to download anything or install proprietary software. You simply have to purchase a user license and login.
Computer-based scenario analysis examples can also be used to study the impact of natural disasters on business and economic activity. These types of scenarios have long-term consequences that you can study in your laboratory or office. Simulations with a natural disaster component can further your research by allowing you to control all aspects of the simulation, including trade, infrastructure, distribution, and consumer behavior. You can then examine the economic and business impacts of different policy decisions.
A well developed scenario analysis should address several important topics: assumptions, impact of unknowns, uncertainty, time course, and response. Each of these topics has a number of different possible solutions depending upon the assumption used. In this section, we will describe several of the most common assumptions used in scenario analysis and sensitivity analysis.
Assumptions can be categorized into three categories: economic, political, and technological. Economic assumptions make up the largest part of scenario planning and sensitivity analysis. Many economic scenarios examine trade scenarios over a number of years.
Political assumptions are typically related to specific types of events. In particular, foreign policy events are usually analyzed using historical data and specific model techniques. Technology assumptions are generally used in forecasting models and are often used in software simulations. It is important to remember that these engineering or scientific models and techniques were developed specifically for particular types of problems. Therefore, the same techniques will not be effective for future stress testing scenarios.
Scenario Analysis Examples
One of the most important aspects of a scenario analysis is the level of uncertainty associated with the inputs to the forecast. Uncertainty is often measured with a statistical confidence level. The numbers associated with a statistical confidence level are referred to as a probability. In most cases, higher the probability, higher is the level of uncertainty and vice versa.
One of the most common scenario modeling assumptions involves the relationship between risk and return. Scenario analysis scenarios are based on a variety of statistical risk metrics and assumptions and therefore assume varying levels of risk. One example of an assumption used in scenario modeling is the rate at which economic variables are changing. Other assumptions can include interest rates, consumption, inflation, government spending, economic growth rates, trade balance (exchange rate), price level, technology growth, geographic area, and political stability.
Another aspect of the best-case scenario analysis is the assumption of a finite number of shocks. The number of such shocks is called the shock probability or simply the incidence or rate of change of any variable. Some of the most common scenarios that include the assumption of a finite number of shocks are recessions, natural disasters, wars, and terrorist attacks. Other factors that might lead to scenarios where the occurrence of a shock is likely are changing demographics, political turmoil, rapid technological change, changes in financial institutions, changes in the composition of the workforce, and other external factors. It is important for the designer of a scenario to consider all possible scenarios and identify which factors most likely to cause the emergence of a problem.
The third section of a macro-financial scenario analysis is what is termed the worst-case scenario. Here, the focus is on the most pessimistic outcome, that is, the financial outcome worse than it is if the scenario were to fail. The main assumption of this category of scenario is that a country will experience a negative output gap, double gross domestic product growth, and interest rates that exceed those associated with safe debt. In order to create these outcomes, the designers of the macro-financial scenarios must include a large amount of uncertainty. A large amount of uncertainty is needed because the designers must consider the possible consequences of the worst case scenario.
The fourth section of a typical scenario analysis involves what is called stress testing. Stress testing is designed to identify the parameters by which the simulation produces the most accurate results. This method is used to identify the inputs by which the simulations determine the output that are necessary for it to be successful. Examples of types of stress testing include the use of historic data, the identification of multiple random variables, and the examination of the parameter values at the end of any historical period.
Scenario analysis examples are used extensively in the financial services world. These are useful because they help people who are responsible for the formulation of business decisions to evaluate the possible outcomes of their choices. Simulations and other situations often lead to surprising conclusions. When people face these conclusions, they often make changes to their strategies or to their plans to ensure that the unexpected does not occur. The accuracy of these situations can provide valuable inputs to those people involved in decision making.
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