Scalable inference for a full multivariate stochastic volatility model

We introduce a multivariate stochastic volatility model that imposes no restrictions on the structure of matrix and treats all its elements as functions latent processes. Inference is achieved via carefully designed feasible scalable MCMC has quadratic, rather than cubic, computational complexity for evaluating normal densities required. illustrate how our can be applied macroeconomic applications through VAR model, comparing it to competing approaches in literature. also demonstrate approach large dataset containing 571 stock daily returns Euro STOXX index.