subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of … increased during the 2008 financial crisis while it has recently returned to its pre-crisis level. The extracted volatility …

conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain … variance, in the order of integration, in the short memory characteristics and in the volatility of volatility. …

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 …

The paper considers the problem as to whether financial returns have a common volatility process in the framework of … stochastic volatility models that were suggested by Harvey et al. (1994). We propose a stochastic volatility version of the ARCH … test proposed by Engle and Susmel (1993), who investigated whether international equity markets have a common volatility …

We investigate high-frequency volatility models for analyzing intra-day tick by tick stock price changes using Bayesian … estimation procedures. Our key interest is the extraction of intra-day volatility patterns from high-frequency integer price … distributions. We allow for stochastic volatility by modeling the variance as a stochastic function of time, with intra-day periodic …

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized …. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions … from returns to volatility and co-volatility. …