含混合高斯项的AR-GJR-GARCH模型的贝叶斯估计  

Bayesian Estimation of AR-GJR-GARCH Model with Mixed Gaussian Terms

作　　者：王祥赛 WANG Xiangsai(School of Mathematics and Statistics,Wuhan University,Wuhan 430072,Hubei,China)

机构地区：[1]武汉大学数学与统计学院,湖北武汉430072

出　　处：《武汉大学学报（理学版）》2022年第3期317-323,共7页Journal of Wuhan University:Natural Science Edition

基　　金：国家自然科学基金(11731012)。

摘　　要：基于不需要后验密度解析形式的随机梯度哈密尔顿蒙特卡洛(stochastic gradient Hamiltonian Monte Carlo,SGHMC)方法对AR-GJR-GARCH模型的参数进行了贝叶斯估计。以2019.3.13—2020.1.2和2020.1.3—2020.11.3两个时间段的中证医药指数的数据为例,对本文提出的方法进行了检验。结果显示,所得的参数估计值反映了与该指数的波动性相关的市场背景信息。Based on the stochastic gradient Hamiltonian Monte Carlo(SGHMC)method which does not require a specific posterior density form,the parameters of AR(autoregressive)-GJR(Glosten-Jagannanthan-Runkle)-GARCH(generalized autoregressive conditional heteroscedasticity)model were estimated by Bayesian method.The method proposed in this paper was tested by taking the data of Chinese Medicine Index from March 13,2019 to January 2,2020 and January 3,2020 to November 3,2020 as exam⁃ples.The results show that the estimated parameters reflect the market background information related to the volatility of the index.

关 键 词：AR-GJR-GARCH模型 贝叶斯估计 哈密尔顿系统 随机梯度哈密尔顿蒙特卡洛 

分 类 号：O212[理学—概率论与数理统计]