A multivariate generalized orthogonal factor GARCH model

We propose a factor generalized autoregressive conditional heteroseedasticity (GARCH) model and develop test procedures for checking the correctness of the number of factors. Maximum likelihood estimation of the model is straightforward once computationally simple preliminary estimates are obtained. Motivated by the empirical application of the article, a mixture of Gaussian distributions is considered in addition to the conventional Gaussian likelihood. Interestingly, some parameters of the conditional covariance matrix that are not identifiable under normality, can be identified when the mixture specification is used. As an empirical example, modeling a system of exchange rate returns and testing for volatility transmission is considered.