Bayesian methods have been efficient in estimating parameters of stochastic volatility models for analyzing financial time series. Recent advances made it possible to fit stochastic volatility models of increasing complexity, including covariates, leverage effects, jump components and...

Bayesian methods have been efficient in estimating parameters of stochastic volatility models for analyzing financial time series. Recent advances made it possible to fit stochastic volatility models of increasing complexity, including covariates, leverage effects, jump components, and...

Stochastic volatility (SV) models provide more realistic and flexible alternatives to ARCH-type models for describing time-varying volatility exhibited in many financial time series. They belong to the wide class of nonlinear state-space models. As classical parameter estimation for SV models is...