Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
利用高效重要性抽样方法，对金融收益率序列的单变量和多变量随机波动率模型进行经典和贝叶斯分析，实现极大似然估计和贝叶斯后验推断。
In this paper, efficient importance sampling (EIS) is used to perform a classical and Bayesian analysis of univariate and multivariate stochastic volatility (SV) models for financial return series. EIS provides a highly generic and very accurate procedure for the Monte Carlo (MC) evaluation of high-dimensional interdependent integrals. It can be used to carry out ML-estimation of SV models as well as simulation smoothing where the latent volatilities are sampled at once. Based on this EIS simulation smoother, a Bayesian Markov chain Monte Carlo (MCMC) posterior analysis of the parameters of SV models can be performed.