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.
The excess kurtosis is kurtosis -3, a way of stating kurtosis relative to the normal distribution, which has a kurtosis of 3.
This model attributes the skewness and excess kurtosis to the jump risk, where jumps occur with a mean annual frequency of 1.
The coefficients of skewness and excess kurtosis prove insignificant at the 5% level.
10) Excess kurtosis means that, compared with the normal distribution, there is excess probability mass in the center of the distribution.
It would exhibit skewness if large, infrequent shocks are typically negative, and excess kurtosis if such shocks are equally likely to be positive or negative.
Because this interval does not require removal of excess kurtosis or assessment of data normality by the Anderson-Darling test, there are fewer cases, compared with the traditional approach, where its computation is not possible.
The major drawback of this approach arises from the fact that returns for individual or set of assets exhibit skewness and significant excess kurtosis (fat-tails and peakness).
The first generator produces normally distributed random numbers, and the second generates Student's t-distributed random numbers with excess kurtosis.
Timmerman argues that his model can explain several stock price (ir)regularities, including skewness, excess kurtosis, volatility clustering, and serial correlation in stock returns.
In sum, the BVMT and TUNINDEX weekly and daily returns tend to be characterized by positive skewness, excess kurtosis and departure from normality.
The tests for normality indicate negative skewness, excess kurtosis and autocorrelation in rate changes.