volatility of US and UK GDP growth appears to have become increasingly correlated in the recent past. …

develop an extended Factor Augmented VAR model that simultaneously allows the estimation of a measure of uncertainty and its …

Through a time-varying VAR model with drifting parameters and stochastic volatilities (Cogley and Sargent, 2005 …

Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are … divergent when volatility clusters idiosyncratically. It is illustrated that this property is important for empirical … multivariate stochastic volatility model is proposed as a robust alternative …

vast model space of time-varying parameter VARs with stochastic volatility and correlated state transitions. This is … VAR coefficient, this new method automatically decides whether it is constant or time-varying. Moreover, it can be used to … shrink an otherwise unrestricted time-varying parameter VAR to a stationary VAR, thus providing an easy way to …

rates and stock returns. In terms of in-sample fit, the VAR model featuring both stochastic volatility and t …In this paper, we provide evidence that fat tails and stochastic volatility can be important in improving in-sample fit … and out-of-sample forecasting performance. Specifically, we construct a VAR model where the orthogonalised shocks feature …

Adding multivariate stochastic volatility of a flexible form to large Vector Autoregressions (VARs) involving over a … literature either works with homoskedastic models or smaller models with restrictive forms for the stochastic volatility. In this … paper, we develop composite likelihood methods for large VARs with multivariate stochastic volatility. These involve …