Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
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DOI：
10.1080/07474930600713424
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发表时间：
2006-09
影响因子：
1.2
通讯作者：
R. Liesenfeld;J. Richard
中科院分区：
文献类型：
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作者：
R. Liesenfeld;J. Richard
本文利用有效重要性抽样（EIS）对金融收益率序列的单变量和多变量随机波动率（SV）模型进行了经典和贝叶斯分析。EIS提供了一个高度通用的和非常准确的程序的Monte Carlo（MC）评估高维相互依赖的积分。它可以用来进行SV模型的ML估计，也可以用来进行一次采样潜在波动率的模拟平滑。基于该EIS仿真平滑器，可以对SV模型的参数进行贝叶斯马尔可夫链蒙特卡罗（MCMC）后验分析。
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.