accounts for time variation in macroeconomic volatility, known as the great moderation. In particular, we consider an … volatility processes and mixture distributions for the irregular components and the common cycle disturbances enable us to … that time-varying volatility is only present in the a selection of idiosyncratic components while the coefficients driving …

volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are …

evidence for stochastic intensity and stochastic volatility models based on Ornstein-Uhlenbeck processes. For our empirical …

general class of discrete-time stochastic volatility (SV) models, characterized by both a leverage effect and jumps in returns … provides a feasible basis for undertaking the nontrivial task of model comparison. Furthermore, we introduce new volatility … model, namely SV-GARCH which attempts to bridge the gap between GARCH and stochastic volatility specifications. In nesting …

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. …

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 …

simulation evidence for stochastic volatility and stochastic intensity models. For our empirical study, we analyse the …