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We provide a simple method to estimate the parameters of multivariatestochastic volatility models with latent factor structures. These models arevery useful as they alleviate the standard curse of dimensionality, allowingthe number of parameters to increase only linearly with the number of thereturn series. Although theoretically very appealing, these models have onlyfound limited practical application due to huge computational burdens. Ourestimation method is simple in implementation as it consists of two steps:first, we estimate the loadings and the unconditional variances by maximumlikelihood, and then we use the efficient method of moments to estimate theparameters of the stochastic volatility structure with GARCH as an auxiliarymodel. In a comprehensive Monte Carlo study we show the good performance of ourmethod to estimate the parameters of interest accurately. The simulation studyand an application to real vectors of daily returns of dimensions up to 148show the method's computation advantage over the existing estimationprocedures.

Sequential Estimation of Multivariate Factor Stochastic Volatility Models