The conditional variance is specified to follow some latent stochastic process in some empirical applications of volatility modelling. Such models are referred to as stochastic volatility (SV) models which were originally proposed by Taylor (1986). The main issue in univariate SV model estimations is that the likelihood function is hard to evaluate because, unlike the estimation of GARCH family models, the maximum-likelihood technique has to deal with more than one stochastic error process. Nevertheless, recently, several new estimation methods such as quasi-maximum likelihood, Gibbs sampling, Bayesian Markov chain Monte Carlo, simulated maximum likelihood have been introduced for univariate models.

I would like to know whether any of aforementioned estimation methods have been extended to multivariate stochastic volatility models? Could anyone recommend any code, package or software with regard to the estimation of multivariate stochastic volatility models?