What does a negative excess kurtosis mean?

What does a negative excess kurtosis mean?

When the value of an excess kurtosis is negative, the distribution is called platykurtic. This kind of distribution has a tail that’s thinner than a normal distribution. The returns on an investment with a leptokurtic distribution or positive excess kurtosis will likely have extreme values.

Which type of distribution shows the negative excess kurtosis?

platykurtic distribution
A platykurtic distribution shows a negative excess kurtosis. The kurtosis reveals a distribution with flat tails. The flat tails indicate the small outliers in a distribution.

How do you interpret excess kurtosis?

If the kurtosis is greater than 3, then the dataset has heavier tails than a normal distribution (more in the tails). If the kurtosis is less than 3, then the dataset has lighter tails than a normal distribution (less in the tails).

What does the kurtosis value tell us?

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.

How can kurtosis be negative?

A negative kurtosis means that your distribution is flatter than a normal curve with the same mean and standard deviation. This means your distribution is platykurtic or flatter as compared with normal distribution with the same M and SD. The curve would have very light tails.

What does Platykurtic distribution mean?

negative
The term “platykurtic” refers to a statistical distribution in which the excess kurtosis value is negative. For this reason, a platykurtic distribution will have thinner tails than a normal distribution will, resulting in fewer extreme positive or negative events.

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.

How do you evaluate skewness and kurtosis?

A general guideline for skewness is that if the number is greater than +1 or lower than –1, this is an indication of a substantially skewed distribution. For kurtosis, the general guideline is that if the number is greater than +1, the distribution is too peaked.

What does it mean when excess kurtosis is negative?

Negative excess kurtosis means that the distribution is less peaked and has less frequent extreme values (less fat tails) than normal distribution. Such distribution is called platykurtic or platykurtotic.

How is the kurtosis of a normal distribution determined?

The kurtosis of a normal distribution equals 3. Therefore, the excess kurtosis is found using the formula below: The types of kurtosis are determined by the excess kurtosis of a particular distribution. The excess kurtosis can take positive or negative values, as well as values close to zero.

Which is an example of excess kurtosis in Excel?

In token of this, often the excess kurtosis is presented: excess kurtosis is simply kurtosis−3. For example, the “kurtosis” reported by Excel is actually the excess kurtosis. A normal distribution has kurtosis exactly 3 (excess kurtosis exactly 0). Any distribution with kurtosis ≈3 (excess ≈0) is called mesokurtic.

What is the relationship between kurtosis and skewness?

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. The histogramis an effective graphical technique for showing both the skewness and kurtosis of data set.