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

TL;DR

This paper introduces a new class of affine invariant, consistent goodness-of-fit tests for multinormality based on the empirical moment generating function, applicable to i.i.d. and GARCH data, with demonstrated finite-sample performance.

Contribution

It extends recent univariate normality tests to the multivariate case, providing a practical, easy-to-use testing procedure with proven asymptotic properties.

Findings

The tests are affine invariant and consistent.

Finite-sample performance compares favorably with existing methods.

Applied successfully to real financial data.

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 $L^2$-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.