We propose an approach for Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural...

We propose an approach for Bayesian inference in \TV SVARs identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant \TV SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to...

This paper examines the importance of realized volatility in bond yield density prediction. We incorporate realized volatility into a Dynamic Nelson-Siegel (DNS) model with stochastic volatility and evaluate its predictive performance on US bond yield data. When compared to popular...