Stochastic volatility models are very flexible models able to characterize financial volatility evolution. This article explores computational capabilities based on Graphical Processing Units to simulate many Monte Carlo Markov chains in estimating stochastic volatility model parameters through...

Stochastic volatility models are very flexible models able to characterize financial volatility evolution. This article explores computational capabilities based on Graphical Processing Units to simulate many Monte Carlo Markov chains in estimating stochastic volatility model parameters through...

Apart from a priori assumptions on instantaneous or long run effects of structural shocks, sign restrictions have become a prominent means for structural vector autoregressive (SVAR) analysis. Moreover, second order heterogeneity of systems of times series can be fruitfully exploited for...

We consider unobserved components time series models where the components are stochastically evolving over time and are subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of the observed time series. We develop a simulated...