Understanding Data Dispersion: From Range to Kurtosis
This article explains key measures of statistical dispersion—including range, mean absolute deviation, variance, standard deviation, coefficient of variation, skewness, and kurtosis—illustrating how each quantifies data spread and why they matter in financial risk analysis.
Knowing the central location of a data set, one may want to know whether the observations are far or near that center; this is called a measure of dispersion. In financial analysis, dispersion is commonly used to assess risk.
Range
The range is defined as max - min . A smaller range indicates lower dispersion. Because it only uses the maximum and minimum values, it ignores the distribution of the remaining data and therefore provides an incomplete picture of variability.
Mean Absolute Deviation
The mean absolute deviation (MAD) is defined as \frac{1}{n}\sum_{i=1}^{n}|x_i - \mu| , where \mu denotes the sample mean and n the number of observations.
Population Variance and Population Standard Deviation
The population variance is defined as \sigma^2 = \frac{1}{N}\sum_{i=1}^{N}(x_i - \mu)^2 , where \mu is the population mean and N the population size.
The population standard deviation is the square root of the variance: \sigma = \sqrt{\sigma^2} .
Sample Variance and Sample Standard Deviation
The sample variance is defined as s^2 = \frac{1}{n-1}\sum_{i=1}^{n}(x_i - \bar{x})^2 , where \bar{x} is the sample mean and n the sample size.
The sample standard deviation is the square root of the sample variance: s = \sqrt{s^2} .
Coefficient of Variation
The coefficient of variation (CV) is defined as the standard deviation divided by the mean: CV = \frac{\sigma}{\mu} .
Skewness
Skewness measures the asymmetry of a data distribution. A perfectly symmetric distribution has a skewness of 0, and its mean, median, and mode are equal. Right‑skewed distributions have mean > median > mode, while left‑skewed distributions have mean < median < mode.
Kurtosis
Kurtosis measures how peaked a distribution is relative to a normal distribution. A normal distribution has kurtosis equal to 3. Excess kurtosis is defined as kurtosis minus 3. Positive excess kurtosis indicates a leptokurtic (high‑peaked) distribution, while negative excess kurtosis indicates a platykurtic (low‑peaked) distribution.
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