What does high excess kurtosis mean?

What does high excess kurtosis mean?

Excess kurtosis. Kurtosis measures the “fatness” of the tails of a distribution. Positive excess kurtosis means that distribution has fatter tails than a normal distribution. Fat tails means there is a higher than normal probability of big positive and negative returns realizations.

What is a good excess kurtosis?

An excess kurtosis above 0 indicates the tails are heavier than the normal distribution. An excess kurtosis below 0 indicates the tails are lighter than the normal distribution. An excess kurtosis value of 1 and above or -1 and below represents a sizable departure from normality.

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 is excess kurtosis in a data distribution?

Excess kurtosis is a metric that compares the kurtosis of a distribution against the kurtosis of a normal distribution. The kurtosis of a normal distribution equals 3.

Can kurtosis be negative?

In statistics, kurtosis is used to describe the shape of a probability distribution. Specifically, it tells us the degree to which data values cluster in the tails or the peak of a distribution. The kurtosis for a distribution can be negative, equal to zero, or positive.

What does a negative kurtosis mean?

A distribution with a negative kurtosis value indicates that the distribution has lighter tails than the normal distribution. For example, data that follow a beta distribution with first and second shape parameters equal to 2 have a negative kurtosis value.

What does it mean if kurtosis is negative?

Negative excess values of kurtosis (<3) indicate that a distribution is flat and has thin tails. Platykurtic distributions have negative kurtosis values. A platykurtic distribution is flatter (less peaked) when compared with the normal distribution, with fewer values in its shorter (i.e. lighter and thinner) tails.

What does negative and positive kurtosis mean?

This definition is used so that the standard normal distribution has a kurtosis of zero. In addition, with the second definition positive kurtosis indicates a “heavy-tailed” distribution and negative kurtosis indicates a “light tailed” distribution.

What does positive and negative kurtosis indicate?

What causes negative kurtosis?

What does negative value of kurtosis mean?

Negative kurtosis: A distribution with a negative kurtosis value indicates that the distribution has lighter tails and a flatter peak than the normal distribution. For example, data that follow a beta distribution with first and second shape parameters equal to 2 have a negative kurtosis value. Peter Westfall.

How to interpret excess kurtosis and skewness?

• Select “descriptive statistics” from the analysis menu.
• From this window,select the variable for which we want to calculate the descriptive statistics and drag them into the variable window.
• How do you interpret skewness and kurtosis in SPSS?

Positive (Right) Skewness Example. A scientist has 1,000 people complete some psychological tests.

• Negative (Left) Skewness Example.
• Symmetrical Distribution Implies Zero Skewness.
• Population Skewness – Formula and Calculation.
• Sample Skewness – Formula and Calculation.
• Skewness in SPSS.
• Skewness – Implications for Data Analysis.
• How do you interpret kurtosis?

– Kaplansky I. A Common Error Concerning Kurtosis. Journal of the American Statistical Association. – Ali MM. Stochastic Ordering and Kurtosis Measure. Journal of the American Statistical Association. – Johnson ME, Tietjen GL, Beckman RJ. A New Family of Probability Distributions With Applications to Monte Carlo Studies.

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