THE SCORE OF CONDITIONALLY HETEROSKEDASTIC DYNAMIC REGRESSION MODELS WITH STUDENT T INNOVATIONS, AN LM TEST FOR MULTIVARIATE NORMALITY
Gabriele Fiorentini (),
Enrique Sentana () and
Working Papers. Serie AD from Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie)
We provide numerically reliable analytical expressions for the score of conditionally heteroskedastic dynamic regression models when the conditional distribution is multivariate $t$. We also derive one-sided and 2-sided LM tests for multivariate normality versus multivariate $t$ based on the first two moments of the (squared) norm of the standardised innovations evaluated at the Gaussian quasi-ML estimators of the conditional mean and variance parameters. We reinterpret them as specification tests for multivariate excess kurtosis, and show that they have power against leptokurtic alternatives. Finally, we analyse UK stock returns, and confirm that their conditional distribution has fat tails.
Keywords: Kurtosis; Inequality Constraints; ARCH; Financial Returns. (search for similar items in EconPapers)
JEL-codes: C51 C52 (search for similar items in EconPapers)
Pages: 32 pages
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http://www.ivie.es/downloads/docs/wpasad/wpasad-2000-33.pdf Fisrt version / Primera version, 2000 (application/pdf)
Journal Article: Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models with Student t Innovations (2003)
Working Paper: The Score of Condionally Heteroskedastic Dynamic Regression Models with Student T Innovations, and an LM Test for Multivariate Normality (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:ivi:wpasad:2000-33
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