Artículo
A new class of tests for multinormality with i.i.d. and garch data based on the empirical moment generating function
Autor/es | Henze, Norbert
Jiménez Gamero, María Dolores |
Departamento | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa |
Fecha de publicación | 2019-06 |
Fecha de depósito | 2019-09-13 |
Publicado en |
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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 ... 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 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. |
Identificador del proyecto | MTM2014-55966-P |
Cita | Henze, 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. |
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