In probability theory and statistics, coskewness is a measure of how much three random variables change together. Coskewness is the third standardized cross central moment, related to skewness as covariance is related to variance. In 1976, Krauss and Litzenberger used it to examine risk in stock market investments. The application to risk was extended by Harvey and Siddique in 2000.
If three random variables exhibit positive coskewness they will tend to undergo extreme deviations at the same time, an odd number of which are in the positive direction (so all three random variables undergoing extreme positive deviations, or one undergoing an extreme positive deviation while the other two undergo extreme negative deviations). Similarly, if three random variables exhibit negative coskewness they will tend to undergo extreme deviations at the same time, an even number of which are in the positive direction (so all three random variables undergoing extreme negative deviations, or one undergoing an extreme negative deviation while the other two undergo extreme positive deviations).
Skewness is a special case of the coskewness when the three random variables are identical:
For two random variables, X and Y, the skewness of the sum, X + Y, is
where SX is the skewness of X and is the standard deviation of X. It follows that the sum of two random variables can be skewed (SX+Y ≠ 0) even if both random variables have zero skew in isolation (SX = 0 and SY = 0).
The coskewness between variables X and Y does not depend on the scale on which the variables are expressed. If we are analyzing the relationship between X and Y, the coskewness between X and Y will be the same as the coskewness between a + bX and c + dY, where a, b, c, and d are constants.
Let X be standard normally distributed and Y be the distribution obtained by setting X=Y whenever X<0 and drawing Y independently from a standard half-normal distribution whenever X>0. In other words, X and Y are both standard normally distributed with the property that they are completely correlated for negative values and uncorrelated apart from sign for positive values. The joint probability density function is
Note that although X and Y are individually standard normally distributed, the distribution of the sum X+Y is significantly skewed. From integration with respect to density, we find that the covariance of X and Y is
from which it follows that the standard deviation of their sum is
Using the skewness sum formula above, we have
This can also be computed directly from the probability density function of the sum:
- Friend, Irwin; Randolf Westerfield (1980). "Co-Skewness and Capital Asset Pricing". The Journal of Finance. 35 (4): 897–913. doi:10.1111/j.1540-6261.1980.tb03508.x.
- Jondeau, Eric; Ser-Huang Poon; Michael Rockinger (2007). Financial Modeling Under Non-Gaussian Distributions. Springer. pp. 31–32. ISBN 978-1-84628-696-4.
- Miller, Michael B. (2014). "Chapter 3. Basic Statistics". Mathematics and Statistics for Financial Risk Management (2nd ed.). Hoboken, New Jersey: John Wiley & Sons, Inc. pp. 53–56. ISBN 978-1-118-75029-2.
- Harvey, Campbell R.; Akhtar Siddique (2000). "Conditional Skewness in Asset Pricing Tests" (PDF). The Journal of Finance. 55 (3): 1263–1295. CiteSeerX 10.1.1.46.5155. doi:10.1111/0022-1082.00247.
- Kraus, Alan; Robert H. Litzenberger (1976). "Skewness Preference and the Valuation of Risk Assets". The Journal of Finance. 31 (4): 1085–1100. doi:10.1111/j.1540-6261.1976.tb01961.x.