model to be extended with stochastic volatility and heavy tailed disturbances. We develop a flexible estimation method for …

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 …

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 …

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 …