I want to understand the survey of Lopes and Polson (2010) regarding MV stochastic volatility. Assume the $p$-dimensional vector $y_t$ follows

$$y_t\sim N(\Theta,\Sigma_t).$$

In order to model the dynamic behavior of the covariance matrix one popular approach is to impose a factor analysis structure of the form

$$\Sigma_t=\beta H_t\beta'+\Psi_t$$

with $\beta$ the $p\times k$ matrix of factor loadings which is lower block triangular with diagonal elements equal to one. $\Psi_t$ and $H_t$ are diagonal covariance matrices.

On page 15 they point out that a large class of factor stochastic volatility models is written as

$$y_t|f_t,\beta,\Sigma_t \sim N(\Theta+\beta f_t;\Psi_t)\\f_t |H_t \sim N(0;H_t)$$

The elements of $\Psi_t$ are modeled by cond. independend univariate SV structures, while $\log h_t=(\log h_{1t},\ldots,\log h_{kt})'$ follows a first-order vector autoregression:

$$\log h_t |h_{t-1},\beta_0,\beta_1,U \sim N(\beta_0+\beta_1 \log h_{t-1},U)$$.

Let's assume conditionally independent components of the priors:


I want to find a way to sample from such a model via MCMC, however, many links are missing. Is there a standard implementation available for that or any paper describing the sampler in some detail? I am happy for each and every comment regarding this project.


1 Answer 1


This book chapter is a pretty good explanation of MCMC techniques for general state space models; you might want to start with this.

For your particular model here, instead of looking at a review paper, you can have a look at this paper, which features a rather involved Metropolis-within-Blocked-Gibbs sampling procedure. It is all detailed in Appendix B. Many of the distributions are multivariate Gaussian, and for those that aren't, they either use a Metropolis-Hastings sampler, or a method discussed in this other paper. This is the other paper that I know of that implements this model, and they use a different Gibbs technique.


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