BASIC CATEGORIES OF RISK

BASIC CATEGORIES OF RISK

With regards insurability, there are basically two categories of risks;

1. Speculative or dynamic risk; and

2. Pure or static risk

Speculative or Dynamic Risk

Speculative (dynamic) risk is a situation in which either profit OR loss is possible. Examples of speculative

risks are betting on a horse race, investing in stocks/bonds and real estate. In the business level, in the daily

Lesson 1 Understanding and Managing Risk 5

conduct of its affairs, every business establishment faces decisions that entail an element of risk. The

decision to venture into a new market, purchase new equipments, diversify on the existing product line,

expand or contract areas of operations, commit more to advertising, borrow additional capital, etc., carry

risks inherent to the business. The outcome of such speculative risk is either beneficial (profitable) or loss.

Speculative risk is uninsurable.

Pure or Static Risk

The second category of risk is known as pure or static risk. Pure (static) risk is a situation in which there are

only the possibilities of loss or no loss, as oppose to loss or profit with speculative risk. The only outcome of pure risks are adverse (in a loss) or neutral (with no loss), never beneficial. Examples of pure risks include premature death, occupational disability, catastrophic medical expenses, and damage to property due to fire, lightning, or flood.

It is important to distinguish between pure and speculative risks for three reasons. First, through the use of commercial, personal, and liability insurance policies, insurance companies in the private sector generally insure only pure risks. Speculative risks are not considered insurable, with some exceptions.

Second, the law of large numbers can be applied more easily to pure risks than to speculative risks. The law of large numbers is important in insurance because it enables insurers to predict loss figures in advance. It is generally more difficult to apply the law of large numbers to speculative risks in order to predict future

Losses. One of the exceptions is the speculative risk of gambling, where casinos can apply the law of large numbers in a very efficient manner.

Finally, society as a whole may benefit from a speculative risk even though a loss occurs, but it is harmed if a

pure risk is present and a loss occurs. For instance, a computer manufacturer's competitor develops a new

technology to produce faster computer processors more cheaply. As a result, it forces the computer

manufacturer into bankruptcy. Despite the bankruptcy, society as a whole benefits since the competitor's

computers work faster and are sold at a lower price. On the other hand, society would not benefit when most pure risks, such as an earthquake, occur.

OTHER RISKS

Besides insurability, there are other classifications of Risks. Few of them are discussed below:

Fundamental Risks and Particular Risks

Fundamental risks affect the entire economy or large numbers of people or groups within the economy.

Examples of fundamental risks are high inflation, unemployment, war, and natural disasters such as earthquakes, hurricanes, tornadoes, and floods.

Particular risks are risks that affect only individuals and not the entire community. Examples of particular

risks are burglary, theft, auto accident, dwelling fires. With particular risks, only individuals experience

losses, and the rest of the community are left unaffected.

The distinction between a fundamental and a particular risk is important, since government assistance may

be necessary in order to insure fundamental risk. Social insurance, government insurance programs, and

government guarantees and subsidies are used to meet certain fundamental risks in our country. For

example, the risk of unemployment is generally not insurable by private insurance companies but can be

insured publicly by federal or state agencies. In addition, flood insurance is only available through and/or

subsidized by the federal government.

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Subjective Risk

Subjective risk is defined as uncertainty based on a person's mental condition or state of mind. For example,

assume that an individual is drinking heavily in a bar and attempts to drive home after the bar closes. The

driver may be uncertain whether he or she will arrive home safely without being arrested by the police for

drunken driving. This mental uncertainty is called subjective risk.

Objective Risk

Objective risk is defined as the relative variation of actual loss from expected loss. For example, assume that

a fire insurer has 5000 houses insured over a long period and, on an average, 1 percent, or 50 houses are

destroyed by fire each year. However, it would be rare for exactly 50 houses to burn each year and in some

years, as few as 45 houses may burn. Thus, there is a variation of 5 houses from the expected number of

50, or a variation of 10 percent. This relative variation of actual loss from expected loss is known as objective

risk.

Objective risk declines as the number of exposures increases. More specifically, objective risk varies

inversely with the square root of the number of cases under observation. Now assume that 5 lacs instead

5000 houses are insured. The expected number of houses that will burn is now 5000, but the variation of actual loss from expected loss is only 50. Objective risk is now 50/5000, or 1 percent.

Objective risk can be statistically measured by some measure of dispersion, such as the standard deviation or coefficient of variation. Since objective risk can be measured, it is an extremely useful concept for an insurance company or a corporate risk manager.

As the number of exposures increases, the insurance company can predict its future loss experience more accurately because it can rely on the “Law of large numbers.” The law of large numbers states that as the

number of exposure units increase, the more closely will the actual loss experience approach the probable

loss experience. For example, as the number of homes under observation increases, the greater is the

degree of accuracy in predicting the proportion of homes that will burn.