The Score of Conditionally Heteroskedastic Dynamic Regression Models with Student T Innovations, and an Lm Test for Multivariate Normality
CEMFI Working Paper No. 0007
Posted: 17 Jan 2001
Date Written: July 2000
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 (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.
JEL Classification: C51, C52
Suggested Citation: Suggested Citation