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What is a scenario analysis?

Scenario analysis is a measure that utilized for the evaluation ofthe combined effect of different variables (scenarios). Since the

variables involved in an investment are interrelated, there are

alters in combination. So, the scenario analysis assists in

analyzing the risk associated with different scenarios. It assists

the decision makers regarding the consideration of different

scenarios and their significance in decision making.

What It Measures ?

Businesses have always planned for what if scenariosand basedtheirdecision-making on likely future events. Scenario analysis is simply aformal tool to model the likelihood of various scenarios and their outcomes.Scenario analysis has been widely used in asset management and riskmanagement since the 1970s, to analyze interest rate risks. However, itcan be applied to a wide set of corporate and financial activities, includingequity prices, exchange rates, commodity prices, and volatility of prices.

Why It Is Important?

Scenario analysis helps companies and investors to measure the expectedvalueof an investment. By combining this information with probability,analysts can make highly accurate predictions about the likelihood ofrealizing the expected value. Comparing the probability distribution of anevent is equivalent to calculating the risk of an investment and important forthe same reasonit allows you to make better decisions about businessand financial investments.

There are many different approaches to scenario analysis, but onecommon method is to determine the high/low spread and standarddeviation from daily or monthly returns, then compute the value of theportfolio if each security generated returns two or three basis points aboveand below this average.

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This method means the investor or company can have reasonable certaintythat the value of a portfolio will remain within expected parameters over agiven period of time.

How It Works in Practice?

A typical use of scenario analysis would be to consider what happens tothe value of a security if interest rates fell, remained static, or increased.

The first step is to consider all the possible outcomes, or paths, that will betaken by relevant risk factors (in this case, interest rates). Each potentialoutcome is considered over the same time period. In the simple examplebelow, we can see all the relevant outcomes that might be affected by a

100 basis point fall in interest rates, followed by 24 months of static rates:

Time

(months)

1-month

LIBOR

3-month

LIBOR

6-month

LIBOR

12-month

LIBOR

COFI

0 3.11 3.79 3.84 4.00 3.54

6 2.11 2.79 3.84 4.00 3.54

12 2.11 2.79 3.84 4.00 3.54

18 2.11 2.79 3.84 4.00 3.54

24 2.11 2.79 3.84 4.00 3.54

Once you have identified the scenario, the next step is to project whathappens for each of these rates at the given point in time. Thisextrapolation may be relatively simple, showing the cost of debt in eachscenario, or might be more complexfor example, by incorporating otherfinancial data to produce a cash flow forecast, or value at risk metric.

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Tricks of the Tradey Scenario analysis allows analysts to plan for their worst-case scenario.

Being able to quantify the potential costs of a possible outcome is vitalin risk management and forming business strategy. Scenario planning canbe highly detailed, and customized to accommodate any number ofvariables, such as the flattening of the yield curve or narrowing spreads.

y One advantage of scenario analysis is that it is simple to use and veryflexible. It can address almost any present or future risk, and can anticipatethe impact of future business decisions. However, its major drawback isthat it can only consider the impact of risks that are anticipatedoutputsare entirely limited to paths suggested by the user, and so if you overlook arisk, it will not be calculated.

y It is also important to remember that if scenario analysis is based on

probability, there is still a potential that the low and high extreme valuescould occur. This is why scenario analysis is often run alongside riskanalysis to determine whether these potential risks are within acceptabletolerance levels.

y When dealing with complex scenarios over multiple time periods andinvolving many possible outcomes (such as the impact on 1,000 clients of100 differentinterest rate permutations) scenario analysis can be extremelycumbersome, generating large and complex tables. In such instances,duration analysis can provide more user-friendly analysis.

y Another alternative is to use scenario analysis and the Monte Carlomethod, to calculate results based on a large number of random scenarios.This approach is sometimes also referred to as simulation analysis.