# Kurtosis

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## Definition of 'Kurtosis'

Kurtosis is a measure of the shape of a probability distribution. It is a measure of the "peakedness" or "flatness" of the distribution. A distribution with a high kurtosis is said to be "peaked" or "leptokurtic", while a distribution with a low kurtosis is said to be "flat" or "platykurtic".

The kurtosis of a distribution is calculated by taking the fourth moment of the distribution and dividing it by the square of the variance. The fourth moment is a measure of the "tailedness" of the distribution. A distribution with a high fourth moment has a long tail, while a distribution with a low fourth moment has a short tail.

The kurtosis of a normal distribution is equal to 3. A distribution with a kurtosis greater than 3 is said to be "leptokurtic", while a distribution with a kurtosis less than 3 is said to be "platykurtic".

Kurtosis is a useful measure of the shape of a distribution because it can help to identify outliers and skewness. Outliers are data points that are significantly different from the rest of the data. Skewness is a measure of the asymmetry of the distribution. A distribution is said to be skewed if it is not symmetrical around the mean.

Kurtosis can be used to identify distributions that are likely to contain outliers or skewness. For example, a distribution with a high kurtosis is more likely to contain outliers than a distribution with a low kurtosis. Similarly, a distribution with a high kurtosis is more likely to be skewed than a distribution with a low kurtosis.

Kurtosis is a useful tool for financial analysts because it can help them to identify potential risks in a portfolio. For example, a portfolio that contains a large number of stocks with a high kurtosis is more likely to experience large losses than a portfolio that contains a large number of stocks with a low kurtosis.

In addition to being used to identify outliers and skewness, kurtosis can also be used to compare the shapes of different distributions. For example, a financial analyst may want to compare the kurtosis of the returns of two different stocks in order to determine which stock is more likely to experience large losses.

Kurtosis is a valuable tool for financial analysts because it can help them to identify potential risks in a portfolio. By understanding the kurtosis of a distribution, a financial analyst can make more informed decisions about the investments that they make.

The kurtosis of a distribution is calculated by taking the fourth moment of the distribution and dividing it by the square of the variance. The fourth moment is a measure of the "tailedness" of the distribution. A distribution with a high fourth moment has a long tail, while a distribution with a low fourth moment has a short tail.

The kurtosis of a normal distribution is equal to 3. A distribution with a kurtosis greater than 3 is said to be "leptokurtic", while a distribution with a kurtosis less than 3 is said to be "platykurtic".

Kurtosis is a useful measure of the shape of a distribution because it can help to identify outliers and skewness. Outliers are data points that are significantly different from the rest of the data. Skewness is a measure of the asymmetry of the distribution. A distribution is said to be skewed if it is not symmetrical around the mean.

Kurtosis can be used to identify distributions that are likely to contain outliers or skewness. For example, a distribution with a high kurtosis is more likely to contain outliers than a distribution with a low kurtosis. Similarly, a distribution with a high kurtosis is more likely to be skewed than a distribution with a low kurtosis.

Kurtosis is a useful tool for financial analysts because it can help them to identify potential risks in a portfolio. For example, a portfolio that contains a large number of stocks with a high kurtosis is more likely to experience large losses than a portfolio that contains a large number of stocks with a low kurtosis.

In addition to being used to identify outliers and skewness, kurtosis can also be used to compare the shapes of different distributions. For example, a financial analyst may want to compare the kurtosis of the returns of two different stocks in order to determine which stock is more likely to experience large losses.

Kurtosis is a valuable tool for financial analysts because it can help them to identify potential risks in a portfolio. By understanding the kurtosis of a distribution, a financial analyst can make more informed decisions about the investments that they make.

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Copyright © 2004-2023, MyPivots. All rights reserved.