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 …

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. …

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 …

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

In this paper we aim to measure actual volatility within a model-based framework using high-frequency data. In the … at smaller and smaller time intervals. High-frequency returns are used for the computation of realised volatility. Recent … theoretical results have shown that realised volatility is a consistent estimator of actual volatility but when it is subject to …