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A new class of tests for multinormality with i.i.d. and garch data based on the empirical moment generating function

  • Norbert Henze [1] ; María Dolores Jiménez-Gamero [2] Árbol académico
    1. [1] Karlsruhe Institute of Technology

      Karlsruhe Institute of Technology

      Stadtkreis Karlsruhe, Alemania

    2. [2] Universidad de Sevilla

      Universidad de Sevilla

      Sevilla, España

  • Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 28, Nº. 2, 2019, págs. 499-521
  • Idioma: inglés
  • DOI: 10.1007/s11749-018-0589-z
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • 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 L2 -statistics, and we provide their asymptotic behavior both for i.i.d. observations and 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


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