Skewness

Skewness is the third centralized moment of a probability density function. It is meant to capture the asymmetry of a distribution. A symmetric distribution (such as the normal distribution) has skewness of zero.

A distribution with a thick right tail and a thin let tail has positive skewness (or will be right skewed), while the opposite is true for a distribution with negative skewness (or one that is let skewed).

Skewness has important ramifications for asset return distributions. Negative skewness is undesirable, since it implies that large, unexpected movements in the asset price are more likely to lead to large losses rather than large gains.

Positive skewness, on the other hand, is more attractive because it implies that large movements in the asset price are likely to lead to large gains. A symmetric distribution implies that large movements are equally likely to lead to large losses or large gains. Many hedge funds, unfortunately, have return distributions that are negatively skewed.

Black–Scholes implied volatilities often exhibit a "skew" when plotted over time. One possible explanation for the volatility skew is that asset prices exhibit skewness, which the normal distribution does not allow.

Skewness
Skewness
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