Stable Distribution of Multivariate Data



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Submitted Oct 5, 2022
Published Aug 31, 2023
10.24018/ejmath.2023.4.4.160

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The main theorem of the paper states that every stable random vector with marginal skewness parameters different from ±1 can be turned into a sub-Gaussian random vector by using an appropriately tailored transformation in multidimensional space. The theorem is used to create a formula on probability density function of stable random vector and to perform a procedure of testing the stable distribution of multivariate data. A dataset collected from the Nasdaq stock market is used to illustrate the proposed procedure.

Keywords: Data analysis Heavy-tailed distribution Portfolio selection Stock market

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