# kurtosis

(redirected from Leptokurtic distribution)
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Related to Leptokurtic distribution: Mesokurtic, skewness, Excess kurtosis

## kur·to·sis

(kər-tō′sĭs)
n. pl. kur·to·ses (-sēz′)
A quantity indicating how sharply a probability distribution function increases and decreases around the distribution's mean.

[Greek kurtōsis, bulging, curvature, from kurtoun, to make bulge, from kurtos, convex; see sker- in Indo-European roots.]

## kurtosis

(kəˈtəʊsɪs)
n
(Statistics) statistics a measure of the concentration of a distribution around its mean, esp the statistic B2 = m4/m22 where m2 and m4 are respectively the second and fourth moment of the distribution around the mean. In a normal distribution B2 = 3. See also platykurtic, mesokurtic, leptokurtic Compare skewness
[from Greek: curvature, from kurtos arched]
Collins English Dictionary – Complete and Unabridged, 12th Edition 2014 © HarperCollins Publishers 1991, 1994, 1998, 2000, 2003, 2006, 2007, 2009, 2011, 2014

## kur•to•sis

(kɜrˈtoʊ sɪs)

n.
a measure of a curve describing the statistical frequency distribution in the region about its mode.
[1900–05; < Greek kýrtōsis convexity]
Translations
koeficient špičatostišpičatost
huipukkuus
kurtosiscoefficient d'aplatissement
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References in periodicals archive ?
However, this method could not cover the leptokurtic distribution after a certain level of exercise prices.
Besides, the kurtosis of Cu and Au is greater than that of the normal distribution (of which the kurtosis value is 3), which results in a leptokurtic distribution for Cu and Au.
These values indicated a positive asymmetry for three negative items (2, 4 and 14), a negative asymmetry for one positive item (6), and a leptokurtic distribution in two items (2 and 6).
The coefficient of kurtosis indicates positive leptokurtic distribution, i.e., higher than normal probability of having extreme values and close to the average.
Extreme tail risks result from the leptokurtic distribution of financial market variables.
In summary, it seems that the Wheat return series is best described by an unconditional leptokurtic distribution and possesses significant conditional hetero skedasticity.
The modeled population is also still slightly platykurtic while the observed population is moving towards a leptokurtic distribution.
The fact that there are very few extreme values, as well as the resemblance with the Gaussian distribution will lead to the conclusion that the resulted distribution will tend to agglomerate near its mean value (for a sufficiently long period of time the Law of Large Numbers (LLN) applies)--the phenomenon is graphically described in Fig.1--keeping the Gaussian shape, but altering the kurtosis parameter of a normal distribution, becoming, in fact, a normal shaped leptokurtic distribution.

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