Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility … unobserved stochastic volatility, and the varying approaches that have been taken for such estimation. In order to simplify the … comprehension of these estimation methods, the main methods for estimating stochastic volatility are discussed, with focus on their …

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility … unobserved stochastic volatility, and the varying approaches that have been taken for such estimation.In order to simplify the … comprehension of these estimation methods, the main methods for estimating stochastic volatility are discussed, with focus on their …

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

ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance densities) areinvestigated in …

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 …

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 …) models, known as the ARCH in Mean (ARCH-M)model. The estimation of ARCH models isrelatively easy compared with that of the …

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

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

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

variable selection, LASSO estimation of threshold autoregressive models, high dimensional stochastic regression with latent …-memory stochastic volatility models, testing for independence between functional time series, statistical inference for panel dynamic … of conditional quantiles in nonlinear time series models, quasi-likelihood estimation of a threshold diffusion process …