Indirect Estimation of Conditionally Heteroskedastic Factor Models

We derive indirect estimators of multivariate conditionally heteroskedastic factor models in which the volatilities of the latent factors depend on their past values. Specifically, we calibrate the analytical score of a Kalman-filter approximation, taking into account the inequality constraints on the auxiliary model parameters. We also study the determinants of the biases in the parameters of this approximation, and its quality. Moreover, we propose sequential indirect estimators that can handle models with large cross-sectional dimensions. Finally, we analyse the small sample behaviour of our indirect estimators and the approximate maximum likelihood procedures through an extensive Monte Carlo experiment.