# What is coefficient of kurtosis?

## What is coefficient of kurtosis?

The coefficient of kurtosis (or also excess kurtosis or just excess) is used to assess whether a density is more or less peaked around its center, than the density of a normal curve and negative values are sometimes used to indicate that a density is flattered around its center than the density of a normal curve.

## How do you interpret the coefficient of kurtosis?

The coefficient of kurtosis (γ2) is the average of the fourth power of the standardized deviations from the mean. For a normal population, the coefficient of kurtosis is expected to equal 3. A value greater than 3 indicates a leptokurtic distribution; a values less than 3 indicates a platykurtic distribution.

What is a good value for kurtosis?

A kurtosis value of +/-1 is considered very good for most psychometric uses, but +/-2 is also usually acceptable. Skewness: the extent to which a distribution of values deviates from symmetry around the mean.

### Why is kurtosis 3?

This heaviness or lightness in the tails usually means that your data looks flatter (or less flat) compared to the normal distribution. The standard normal distribution has a kurtosis of 3, so if your values are close to that then your graph’s tails are nearly normal.

### What is acceptable skewness and kurtosis?

The values for asymmetry and kurtosis between -2 and +2 are considered acceptable in order to prove normal univariate distribution (George & Mallery, 2010). (2010) and Bryne (2010) argued that data is considered to be normal if skewness is between ‐2 to +2 and kurtosis is between ‐7 to +7.

What does a high kurtosis mean?

Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. Data sets with low kurtosis tend to have light tails, or lack of outliers. A uniform distribution would be the extreme case.

## What is the kurtosis of a normal distribution?

The standard normal distribution has a kurtosis of 3, so if your values are close to that then your graph’s tails are nearly normal. These distributions are called mesokurtic. Kurtosis is the fourth moment in statistics.

## Is high kurtosis good or bad?

Kurtosis is only useful when used in conjunction with standard deviation. It is possible that an investment might have a high kurtosis (bad), but the overall standard deviation is low (good). Conversely, one might see an investment with a low kurtosis (good), but the overall standard deviation is high (bad).

What happens when the kurtosis coefficient is above normal?

It is an important consideration to take when examining historical returns from a particular stock or portfolio. The higher the kurtosis coefficient is above the normal level—or the fatter the tails on the return distribution graph—the more likely that future returns will be either extremely large or extremely small.

### How is the moment ratio used to measure kurtosis?

Moment ratio and Percentile Coefficient of kurtosis are used to measure the kurtosis where Q.D = 1 2 ( Q 3 – Q 1) is the semi-interquartile range. For normal distribution this has the value 0.263. The kurtosis parameter is a measure of the combined weight of the tails relative to the rest of the distribution.

### Why is kurtosis an important measure of skewness?

Kurtosis is a statistical measure that defines how heavily the tails of a distribution differ from the tails of a normal distribution. In other words, kurtosis identifies whether the tails of a given distribution contain extreme values. Along with skewness, kurtosis is an important descriptive statistic of data distribution.

What are the different types of excess kurtosis?

Types of Kurtosis. 1 1. Mesokurtic. Data that follows a mesokurtic distribution shows an excess kurtosis of zero or close to zero. This means that if the data follows a 2 2. Leptokurtic. 3 3. Platykurtic.