This work deals with multivariate stochastic volatility models, which account for a time-varying variance … the volatility level. We apply a full Bayesian inference approach, which relies upon Sequential Monte Carlo (SMC) for …

The goal of this article is an exact Bayesian analysis of the Heston (1993) stochastic volatility model. We carefully … study the effect different parameterizations of the latent volatility process and the parameters of the volatility process …

dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian … risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. …

-varying parameter vector autoregression with stochastic volatility developed by Cogley and Sargent (2005) and Primiceri (2005), CSP …

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. …

wide variety of stocks, bonds and options. Evidence suggests that both the expected return and the volatility vary over … considerable effort has been devoted to the modelling of time-varying volatility. Recent attention has moved to examining the … daily stock market volatility in a sample of significant emerging stock markets using an Asymetric Volatility Model (ASV …

gap inflation persistence to a UC model that already has stochastic volatility (SV) afflicting trend and gap inflation …

-time version of the stochastic volatility (SV) model. The Bayesian approach represents a feasible way to estimate SV models. Under …

drifting gap inflation persistence to a UC model in which stochastic volatility (SV) affects trend and gap inflation. Another …