Most multivariate variance models suffer from a common problem, the "curse of
dimensionality". For this reason, most are fitted under strong parametric restrictions that
reduce the interpretation and flexibility of the models. Recently, the literature has
focused on multivariate models with milder restrictions, whose purpose was to combine
the need for interpretability and efficiency faced by model users with the computational
problems that may emerge when the number of assets is quite large. We contribute to
this strand of the literature proposing a block-type parameterization for multivariate
stochastic volatility models.
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