The persistent nature of equity volatility is investigated by means of a multi-factor stochastic volatility model with … model a time-varying generalization of the HAR model for the realized volatility series. It emerges that during the recent … stochastic volatility model suggest that the change in the dynamic structure of the realized volatility during the financial …

estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be … extended to models with time-varying VMA coefficients and stochastic volatility. We illustrate the methodology through a … macroeconomic forecasting exercise. We show that in a class of models with stochastic volatility, VARMAs produce better density …

We introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional … equation are no longer serially independent, and estimation becomes more difficult. We develop a posterior simulator that … involving U.S. inflation we find that these moving average stochastic volatility models provide better in sample fitness and out …

dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian … momentum strategy. The estimation of this modeling and strategy approach can be done using an extended and modified version of … risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. …

components, and innovations to trend and gap inflation are affected by stochastic volatility. A novelty of our model is to allow …

factor. Second, we specify the overall volatility as a generalized autoregressive conditional heteroscedasticity (GARCH …

We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors …

section compares stochastic volatility models with GARCH. …