forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures …

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility … unobserved stochastic volatility, and the varying approaches that have been taken for such estimation.In order to simplify the … comprehension of these estimation methods, the main methods for estimating stochastic volatility are discussed, with focus on their …

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 …

methods. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed …

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

methods. The effects ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance …

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 maximum likelihood estimation method based on importance sampling and … increased during the 2008 financial crisis while it has recently returned to its pre-crisis level. The extracted volatility …

estimation of static factor models and factor augmented autoregressions using a set of 190 quarterly observations of 144 US …