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Title: Fourth cumulant for the random sum of random vectors Authors:  Farrukh Javed - Orebro University (Sweden) [presenting]
Nicola Loperfido - University of Urbino (Italy)
Stepan Mazur - Orebro University (Sweden)
Abstract: The fourth cumulant for the aggregated multivariate claims is considered. A formula is presented for the general case when the aggregating variable is independent of the multivariate claims. Two important special cases are considered. In the first one, multivariate skewed normal claims are considered and aggregated by a Poisson variable. For the Poisson Skew-normal case, we also proved that the kurtosis of a linear projection of aggregated multivariate claims attains its maximum when the projecting direction is the same as the shape parameter $\mathbf{\alpha }$. The second case is dealing with multivariate asymmetric generalized Laplace and aggregation is made by a negative binomial variable. Due to the invariance property, the latter case can be derived directly, leading to the identity involving the cumulant of the claims and the aggregated claims. There is a well-established relationship between asymmetric Laplace motion and negative binomial process that corresponds to the invariance principle of the aggregating claims for the generalized asymmetric Laplace distribution. We explore this relationship and provide multivariate continuous time version of the results. It is discussed how these results that deal only with dependence in the claim sizes can be used to obtain a formula for the fourth cumulant for more complex aggregate models of multivariate claims in which the dependence is also in the aggregating variables.