innovations and the variables themselves. By formulating the MSVAR as an extended linear non-Gaussian VAR for the combination of …

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. …

When analysing the volatility related to high frequency financial data, mostly non-parametric approaches based on … stochastic volatility. Estimation of the model delivers measures of daily variation outperforming their non …

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 …

In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean …(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable … Stochastic Volatility (SV)model. However, efficient Monte Carlo simulationmethods for SV models have been developed to overcome …

implied by option prices. We develop anSV model with implied volatility as an exogeneous var able in the varianceequation …In this paper we compare the predictive abilility of Stochastic Volatility (SV)models to that of volatility forecasts … stochastic shocks incorporated in the SVXmodels. The out-of-sample volatility forecasts are evaluated against dailysquared …