Skewness and kurtosis are important characteristics of probability distributions that provide information about the shape and behavior of the distribution.
Skewness measures the degree of symmetry of a distribution. A perfectly symmetrical distribution has a skewness of 0. A positive skewness indicates that the tail of the distribution is longer on the positive side than on the negative side, while a negative skewness indicates the opposite. In finance and investing, skewness is important because it can indicate the potential for extreme outcomes. For example, a positively skewed distribution may indicate that there is a greater likelihood of experiencing large losses than large gains.
Kurtosis, on the other hand, measures the degree of peakedness or flatness of a distribution. A normal distribution, with a bell-shaped curve, has a kurtosis of 3. A distribution with a kurtosis greater than 3 is said to be leptokurtic, indicating that it has more scores in the tails than a normal distribution. A distribution with a kurtosis less than 3 is said to be platykurtic, indicating that it has fewer scores in the tails than a normal distribution. High kurtosis distributions are important in finance and investing because they can indicate periods of high volatility and unexpected events that can have significant impact on the underlying asset.
In summary, skewness and kurtosis are important characteristics of probability distributions that provide valuable information about the shape and behavior of the distribution. Finance and investing professionals can use these measures to better understand the potential risks and outcomes associated with different investment strategies and asset classes.