[1]赵慧琴,刘金山.采用MCMC方法的上海股市随机波动模型[J].华侨大学学报(自然科学版),2017,38(2):262-265.[doi:10.11830/ISSN.1000-5013.201702024]
　ZHAO Huiqin,LIU Jinshan.Stochastic Volatility Modeling of Shanghai Stock Exchange Using MCMC Method[J].Journal of Huaqiao University(Natural Science),2017,38(2):262-265.[doi:10.11830/ISSN.1000-5013.201702024]
点击复制

采用MCMC方法的上海股市随机波动模型()
分享到：

《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第38卷
期数:
2017年第2期
页码:
262-265
栏目:
出版日期:
2017-03-20

文章信息/Info

Title:
Stochastic Volatility Modeling of Shanghai Stock Exchange Using MCMC Method
文章编号:
1000-5013(2017)02-0262-04
作者:
赵慧琴1;  刘金山2
1. 广东财经大学 华商学院, 广东 广州 511300;2. 华南农业大学 数学与信息学院, 广东 广州 510642
Author(s):
ZHAO Huiqin1;  LIU Jinshan2
1. Huashang College, Guangdong University of Business Studies, Guangzhou 511300, China; 2. College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
关键词:
随机波动率模型;  马尔科夫链-蒙特卡罗方法;  股市波动;  贝叶斯分析;  上海股市
Keywords:
stochastic volatility models;  Markov chain Monte Carlo method;  stochastic volatility;  Bayesian analysis;  Shanghai Stock
分类号:
O212
DOI:
10.11830/ISSN.1000-5013.201702024
文献标志码:
A
摘要:
采用贝叶斯统计中的马尔科夫链-蒙特卡罗(MCMC)方法对上海股市的随机波动性进行研究,基于Gibbs抽样的MCMC数值计算过程,对上海股市的随机波动率模型(SV)进行参数估计,并在WinBUGS软件中实现.根据信息判别准则(DIC),对比拟合的SV-N,SV-T,SV-MT模型参数,结果表明:SV-T模型最能反映上海股市波动具有尖峰厚尾的特性,可进一步用于预测样本外的波动率结果.
Abstract:
One method is by Markov chain Monte Carlo(MCMC)bias statistics method. In this paper, we study the stochastic volatility of Shanghai Stock market, and estimate the parameters of the stochastic volatility model(SV)of Shanghai Stock market based on the MCMC sampling, and implement the Gibbs software in the WinBUGS software. By comparingthe parameters of SV-N, SV-T, SV-MT model, and according to discriminative information criterion, we find the SV-T model is the best model in China reflecting the fluctuation of the stock market of Shanghai which has peak thick tail characteristics, this model can also be used to step out of sample forecasting volatility results.

参考文献/References:

[1] NAKAJIMA J,OMORI Y.Leverage, heavy-tails and correlated jumps in stochastic volatility models[J].Computational Statistics and Data Analysis,2009,53(6):2535-2553.
[2] BROTO C,RUIZ E.Estimation methods for stochastic volatility models: A survey[J].Journal of Economic Surveys,2004,18(5):613-649.
[3] WANG J J J,CHAN J S K,BORIS C S T.Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures[J].Computational Statistics and Data Analysis,2011,55(1):852-862.
[4] 王泽锋,史代敏.基于DIC准则的ASV模型和SV模型的实证比较[J].数量经济技术经济研究,2007,24(5):134-141.
[5] 顾锋娟,岑仲迪.基于GADCH类模型和SV类模型的泸深两市波动性研究[J]. 数学的实践与认识,2011,41(1):14-22.
[6] 朱慧明,李峰,杨锦明.基于MCMC 模拟的贝叶斯厚尾金融随机波动模型分析[J].运筹与管理,2007,16(4):111-115.
[7] 何永涛,王飞.随机波动模型贝叶斯估计效率研究[J].统计与信息论坛,2015,30(4):7-14.
[8] TAYLOR S J.Modeling stochastic volatility[J].Mathematical Finance,1994,4(2):183-204.
[9] KIM S,SHEPHARD N,CHIB S.Stochastic volatility: Likelihood inference andcomparison with ARCH models[J].Review of Economic Studies,1998,65(3):361-393.
[10] MEYER R,YU Jun.BUGS for a Bayesian analysis of stochastic volatility models[J].Econometrics Journal,2000,3(2):198-215.
[11] SPIEGELHALTER D J,BEST N,CARLIN P B.Bayesian measurements of model flexibility and fit(with discussion)[J].Journal of Royal Statistical Society,2002,64(4):583-639.
[12] 蔡晓薇.MCMC方法下最优Copula的估计及选取[J].统计与信息论坛,2011,26(10):33-38.
[13] 邱世斌,陈燕武.马尔可夫链在前沿资产组合中的应用[J].华侨大学学报(自然科学版),2009,30(2):221-224.
[14] LSHIHARA T,OMORI Y,ASAI M.Matrix exponential stochastic volatility with cross leverage[J].Comuputational Statistics and Data Analysis,2016,100(8):331-350.
[15] SO M K P,KWOK S W Y.A multivariate long memory stochastic volatility model[J].Physica A,2006,362(2):450-464.
[16] HO H C.Sameple quantile analysis for long-memory stochastic volatility models[J].Journal of Econometicle,2015,189(2):360-370.

备注/Memo

备注/Memo:
收稿日期: 2017-02-14
通信作者: 赵慧琴(1982-),女,讲师,博士,主要从事经济统计和概率统计的研究.E-mail:zhq_6285144@163.com.
基金项目: 广东省青年创新人才类重点课程建设项目(2014WQNCX177); 广东省质量工程经管综合实验教学中心建设项目(2013年度); 广东省质量工程统计学专业实验教学示范中心建设项目(2014年度)
更新日期/Last Update: 2017-03-20