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. …

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

simulation algorithm exists for this process, at present this is not the case for the Heston stochastic volatility model, where …

improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source … for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … conjunction with a variety of volatility models for returns on the Standard & Poor's 100 stock index. We consider two so …

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

The characteristic functions of many affine jump-diffusion models, such as Heston’s stochastic volatility model and all …

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 …

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