Henze, NorbertJiménez Gamero, María Dolores2019-09-132019-09-132019-06Henze, N. y Jiménez Gamero, M.D. (2019). A new class of tests for multinormality with i.i.d. and garch data based on the empirical moment generating function. TEST, 28 (2), 499-521.1133-06861863-8260https://hdl.handle.net/11441/89115We generalize a recent class of tests for univariate normality that are based on the empirical moment generating function to the multivariate setting, thus obtaining a class of affine invariant, consistent and easy-to-use goodness-of-fit tests for multinormality. The test statistics are suitably weighted L2-statistics, and we provide their asymptotic behavior both for i.i.d. observations as well as in the context of testing that the innovation distribution of a multivariate GARCH model is Gaussian. We study the finite-sample behavior of the new tests, compare the criteria with alternative existing procedures, and apply the new procedure to a data set of monthly log returns.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Moment generating functionGoodness-of-fit testMultivariate normalityGaussian GARCH modelinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1007/s11749-018-0589-z