Risk

Risk is the possibility of a loss. Risk sometimes denotes an object that is a cause of risk, or a person or property that would be risky to insure. Thus, a heavy drinker would probably be a risk as a driver, or a wooden building would be a poor risk for fire insurance.

Subjective risk is what an individual perceives to be a possible unwanted event. Most people realize, for instance, that it’s possible for them to have an accident, or a heart attack or some other health problem. Or that they will lose money buying lottery tickets. How much subjective risk people experience depends on their history and their expected possibility of its occurrence—subjective probability. Somebody who has lost a lot of money in the stock market will probably feel more risk investing in the market than someone who has profited handsomely. Subjective risk may alter the behavior of the risk taker if it is a very undesirable risk, or one that has a good chance of occurring if something is done. Thus, someone who was in a bad auto accident might drive much more carefully than someone who has never been in one.

Insurance is the transference of financial loss due to risk to a company or other organization. The company accepts this transference for a periodic premium, and profits by collecting more in premiums and making more from the investments of those premiums than it pays out in claims, which are payments to the insured for the losses they incurred.

Insurance companies can only make a profit if they understand risk and the frequency of its occurrence. One way to study risk is to observe the actual number of losses to the total possible. Take a sample of 10,000 houses that were built many years ago, for instance. An expected frequency of fires can be calculated by learning how many houses burned each year, then averaging those numbers. If this average is 10, for instance, then this is the expected loss. However, it will be rare that exactly 10 houses burn each year. There may be years when none, 6, 12, or 17 of them burn. Objective risk (aka degree of risk) is the actual losses for a sample for a given period.

Objective loss is also the variation of actual losses from expected losses, and is inversely proportional to the square root of the sample size—the law of large numbers. For example, if you flip a coin 10 times, it is expected that 5 of those flips will yield heads and the other 5 will yield tails. However, in most sets of 10 flips the actual number of heads and tails will differ from this expectation, and it may differ significantly. It’s possible that all will be heads, for instance. However, as the number of flips is increased, the number of heads and tails will tend toward equality. In 1,000,000 flips, it is highly unlikely that they will all be heads or all tails, and, in fact, the number of both will be closer to the mean.

Chance of loss is the probability that a loss will occur.

Objective probability is the probability of an occurrence based on either reasoning or by actual observations of a large number of similar events taking place in similar conditions. A priori probability is a probability calculated by determining the ratio of a given outcome to finite possibilities. Thus, there is a 50% chance that a perfectly balanced coin will come up heads if flipped, or a 1/6 chance that a 2 will come on top of a rolled, perfectly balanced and shaped, die.

For more complicated scenarios, an objective probability can be deduced from a large number of observations under a given set of conditions. Thus, the probability that a 50-year old man will live to 65 cannot be calculated a priori, but can be based on the proportion of 50-year old men who have lived to 65 in the past.

Subjective probability is a person’s perception of the likelihood of the event. Most of the time, subjective probability differs significantly from objective probability, either because the person cannot calculate the actual probability or because the person feels lucky or unlucky, or because they think they can rig the game. Of course, if people had a better assessment of objective probability, few people would be playing the lottery or gambling, except for those individuals who are feeling lucky, or because they know how to obtain better odds by doing specific things, such as counting cards at Black Jack.

In insurance, a chance of loss is distinguished from objective risk in that objective risk depends on actual losses compared to expected losses for a given population, whereas the chance of loss is only depended on the mean, which is the expected loss. Thus, 10,000 homes in Florida can have the same chance of loss as 10,000 homes in Georgia, but there could be greater differences from the mean in most years in 1 area over another.

Search this financial encyclopedia by using the search engine below.

Custom Search

◄ Share or bookmark this page on several major sites.

Information is provided 'as is' and solely for education, not for trading purposes or professional advice.