Sequential Estimation of Shape Parameters in Multivariate Dynamic Models

Sequential maximum likelihood and GMM estimators of distributional parameters obtained from the standardised innovations of multivariate conditionally heteroskedastic dynamic regression models evaluated at Gaussian PML estimators preserve the consistency of mean and variance parameters while allowing for realistic distributions. We assess the efficiency of those estimators, and obtain moment conditions leading to sequential estimators as efficient as their joint maximum likelihood counterparts. We also obtain standard errors for the quantiles required in VaR and CoVaR calculations, and analyse the effects on these measures of distributional misspecification. Finally, we illustrate the small sample performance of these procedures through Monte Carlo simulations.