Like skewness, kurtosis is a statistical measure that is used to describe distribution. Whereas skewness differentiates extreme values in one versus the other tail, kurtosis measures extreme values in either tail. Distributions with large kurtosis exhibit tail data exceeding the tails of the normal distribution (e.g., five or more standard deviations from the mean). Distributions with low kurtosis exhibit tail data that are generally less extreme than the tails of the normal distribution.
For investors, high kurtosis of the return distribution implies the investor will experience occasional extreme returns (either positive or negative), more extreme than the usual + or – three standard deviations from the mean that is predicted by the normal distribution of returns. This phenomenon is known as kurtosis risk.
Kurtosis is a measure of the combined weight of a distribution’s tails relative to the center of the distribution. When a set of approximately normal data is graphed via a histogram, it shows a bell peak and most data within three standard deviations (plus or minus) of the mean. However, when high kurtosis is present, the tails extend farther than the three standard deviations of the normal bell-curved distribution.
Kurtosis is sometimes confused with a measure of the peakedness of a distribution. However, kurtosis is a measure that describes the shape of a distribution’s tails in relation to its overall shape. A distribution can be infinitely peaked with low kurtosis, and a distribution can be perfectly flat-topped with infinite kurtosis. Thus, kurtosis measures “tailedness,” not “peakedness.”
There are three categories of kurtosis that can be displayed by a set of data. All measures of kurtosis are compared against a standard normal distribution, or bell curve.
The first category of kurtosis is a mesokurtic distribution. This distribution has a kurtosis statistic similar to that of the normal distribution, meaning the extreme value characteristic of the distribution is similar to that of a normal distribution.
The second category is a leptokurtic distribution. Any distribution that is leptokurtic displays greater kurtosis than a mesokurtic distribution. Characteristics of this distribution is one with long tails (outliers.) The prefix of “lepto-” means “skinny,” making the shape of a leptokurtic distribution easier to remember. The “skinniness” of a leptokurtic distribution is a consequence of the outliers, which stretch the horizontal axis of the histogram graph, making the bulk of the data appear in a narrow (“skinny”) vertical range. Thus leptokurtic distributions are sometimes characterized as “concentrated toward the mean,” but the more relevant issue (especially for investors) is there are occasional extreme outliers that cause this “concentration” appearance. Examples of leptokurtic distributions are the T-distributions with small degrees of freedom.
The final type of distribution is a platykurtic distribution. These types of distributions have short tails (paucity of outliers.) The prefix of “platy-” means “broad,” and it is meant to describe a short and broad-looking peak, but this is an historical error. Uniform distributions are platykurtic and have broad peaks, but the beta (.5,1) distribution is also platykurtic and has an infinitely pointy peak. The reason both these distributions are platykurtic is their extreme values are less than that of the normal distribution. For investors, platykurtic return distributions are stable and predictable, in the sense that there will rarely (if ever) be extreme (outlier) returns.