Multivariate stochastic volatility with co-heteroscedasticity

This paper develops a new methodology that decomposes shocks into homoscedastic and heteroscedastic components. This specification implies there exist linear combinations of heteroscedastic variables that eliminate heteroscedasticity. That is, these linear combinations are homoscedastic; a property we call co-heteroscedasticity. The heteroscedastic part of the model uses a multivariate stochastic volatility inverse Wishart process. The resulting model is invariant to the ordering of the variables, which we show is important for impulse response analysis but is generally important for, e.g., volatility estimation and variance decompositions. The specification allows estimation in moderately high-dimensions. The computational strategy uses a novel particle filter algorithm, a reparameterization that substantially improves algorithmic convergence and an alternating-order particle Gibbs that reduces the amount of particles needed for accurate estimation. We provide two empirical applications; one to exchange rate data and another to a large Vector Autoregression (VAR) of US macroeconomic variables. We find strong evidence for co-heteroscedasticity and, in the second application, estimate the impact of monetary policy on the homoscedastic and heteroscedastic components of macroeconomic variables.