%0 Journal Article
%T 基于Griddy-Gibbs抽样的混合高斯AR-GJR-GARCH模型的贝叶斯估计<br>Bayesian estimation of the Gaussian mixture AR-GJR-GARCH model with Griddy-Gibbs sampler
%A 张新星
%A 唐亚勇
%J 四川大学学报 (自然科学版)
%D 2016
%X 综合考虑波动率的尖峰厚尾性、杠杆效应、自回归条件异方差性以及收益率的自回归性等特点，作者提出了混合高斯AR-GJR-GARCH模型，并用基于Griddy-Gibbs抽样的MCMC方法对模型的参数进行了贝叶斯估计, 以新东方的股票市场为例用Matlab和R软件对模型进行了实现与检验. 模型对波动率的各种特性都有一定的体现，并且估计方法的收敛速度较快、自相关性弱、算法复杂度低、稳定性良好.<br>Considering the characteristics of the volatility such as excess kurtosis and leverage effect, the authors propose a Gaussian mixture AR-GJR-GARCH model. The parameters of the model are estimated by using MCMC method based on Griddy-Gibbs sampler. The model is implemented and tested by Matlab and R software taking EDU stock market as an example. The method has a certain manifestation on the characteristics of the volatility and the method has the good convergence, the weak autocorrelation, the simple algorithm, and the nice stability
%K 混合高斯分布
%K AR-GJR-GARCH模型
%K Griddy-Gibbs抽样
%K MCMC方法&lt
%K br&gt
%K Gaussian Mixture distribution
%K AR-GJR-GARCH model
%K Griddy-Gibbs sampler
%K MCMC method
%U http://science.ijournals.cn/jsunature_cn/ch/reader/view_abstract.aspx?file_no=Z150593&flag=1