Excess kurtosis


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Related to Excess kurtosis: platykurtic, leptokurtic, Curtosis

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. When calculating kurtosis, a result of +3.00 indicates the absence of kurtosis (distribution is mesokurtic). For simplicity in its interpretation, some statisticians adjust this result to zero (i.e. kurtosis minus 3 equals zero), and then any reading other than zero is referred to as excess kurtosis. Negative numbers indicate a platykurtic distribution; positive numbers indicate a leptokurtic distribution.

Excess Kurtosis

A measure of the fatness of the tails of kurtosis where there is higher likelihood of large gains or large losses on an investment. That is, excess kurtosis indicates that the volatility of the investment is itself highly volatile.
References in periodicals archive ?
For the explanatory variables, Panels A and B show that the skewness and kurtosis values of advertising, R&D and control variables are skewed and have excess kurtosis.
Far from normal where the excess kurtosis would be 0.
Compared to the BS model, the jump diffusion model attributes the skewness and excess kurtosis observed in the implied distribution of the underlying asset returns to the random jumps in the underlying asset returns.
Both series are not normally distributed; the changes in the NZVIX have a high excess kurtosis, while the NZX 15 returns are negatively skewed.
The standardized residuals exhibit symmetric distributions, but with significant excess kurtosis.
Normality is also rejected since there are still problems with excess skewness and excess kurtosis.
The change in the real interest rate exhibits statistically significant excess kurtosis (5.
2 shows an example of a symmetric distribution that has a positive coefficient of excess kurtosis relative to a normal distribution.
For the monthly forecast horizon there is no excess kurtosis, even for the raw data.
Second, we look for signs of large shocks, and associated skewness and excess kurtosis, in the relevant distributions.
A further transform is applied, if necessary, to remove residual excess kurtosis (5).