Mesokurtic is a statistical term used to describe the outlier (or rare, extreme data) characteristic of a probability distribution. A mesokurtic distribution has a similar extreme value character as a normal distribution. Kurtosis is a measure of tails, or extreme values, of a probability distribution. With greater kurtosis, extreme values (e.g., values five or more standard deviations from the mean) occasionally occur.

## Breaking Down Mesokurtic

Distributions may be described as mesokurtic, platykurtic and leptokurtic. Mesokurtic distributions have a kurtosis of zero, matching that of the normal distribution, or normal curve, also known as a bell curve. In contrast, a leptokurtic distribution has fatter tails. This means that the probability of extreme events is greater than that implied by the normal curve. Platykurtic distributions, on the other hand, have lighter tails, and the probability of extreme events is lesser than that implied by the normal curve. In finance, the probability of an extreme event that is negative is called "tail risk."

Risk managers also must be concerned about probability distributions with "long tails." In a distribution with a long tail, the probability of a highly extreme event is non-negligible.

Kurtosis is an important concept in finance because it affects risk management. Investment returns are assumed to be distributed normally, that is, to be distributed in a normal, bell-shaped curve. In reality, returns fall into a leptokurtic distribution, with "fatter tails" than the normal curve. This means that the probability of large losses or large gains is greater than would be expected if returns matched a normal curve.