This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model.It establishes that systematically different dynamic restrictions are imposed whenthe ratio of volatilities is time-varying. Simulations demonstrate that estimated co-variance matrices become more divergent when volatility clusters idiosyncratically.It is illustrated that this property is important for empirical applications. Specifically, alternative estimates on the evolution of U.S. systematic monetary policy andinflation-gap persistence indicate that conclusions may critically hinge on a selectedordering of variables. The dynamic correlation Cholesky multivariate stochasticvolatility model is proposed as a robust alternative.
Model uncertainty Multivariate stochastic volatility Dynamic correlations Monetary policy Structural VAR