The Score of Condionally Heteroskedastic Dynamic Regression Models with Student T Innovations, and an LM Test for Multivariate Normality
Gabriele Fiorentini (),
Enrique Sentana () and
Working Papers from Centro de Estudios Monetarios Y Financieros-
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 two-sided LM tests for multivariate normality versus multivariate t based on the first two moments of the 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 lepkurtic alternatives. Finally, we analyse UK stock returns, and confirm that their conditional distribution has fat tails.
Keywords: MODELS; TESTS; DISTRIBUTION (search for similar items in EconPapers)
JEL-codes: C52 C52 (search for similar items in EconPapers)
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Journal Article: Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models with Student t Innovations (2003)
Working Paper: THE SCORE OF CONDITIONALLY HETEROSKEDASTIC DYNAMIC REGRESSION MODELS WITH STUDENT T INNOVATIONS, AN LM TEST FOR MULTIVARIATE NORMALITY (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:fth:cemfdt:0007
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