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Excess kurtosis of a univariate random variable is defined as its kurtosis minus 3, i.e. the kurtosis of a normal distribution. Excess kurtosis is a one of a dispersion measures. This parameter provides the information about peakedness and tail weight of a distribution compared to normal distribution. In the paper we propose a generalization of this characteristic for random vectors and analyze its basic properties. Moreover, we introduce the form of excess kurtosis for the selected multivariate distribution.
EN
In the previous articles the authors proposed - different from the well-known in the probabilistic literature - definitions of such characteristics of multivariate probability distributions as the expected value, variance, standard deviation, skewness coefficient, kurtosis and excess kurtosis. The basis of these definitions is the concept of the power of the vector in an inner product space proposed by J. Tatar, among others things, in Tatar (1996b). In this paper, the formal forms of those which are mentioned above are used to describe some random vectors occurring in a typical financial market. In this case these.
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