A new class of tests for multinormality with i.i.d. and Garch data based on the empirical moment generating function
Abstract
We 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 -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.
Keywords
Cite
@article{arxiv.1711.07199,
title = {A new class of tests for multinormality with i.i.d. and Garch data based on the empirical moment generating function},
author = {Norbert Henze and María Dolores Jiménez-Gamero},
journal= {arXiv preprint arXiv:1711.07199},
year = {2017}
}
Comments
27 pages, 2 figures. arXiv admin note: text overlap with arXiv:1706.03029