基于贝叶斯Wishart波动模型的原油市场与股市动态相依性研究

中国管理科学 ›› 2014, Vol. 22 ›› Issue (7): 1-9.

• 论文 • 下一篇

基于贝叶斯Wishart波动模型的原油市场与股市动态相依性研究

朱慧明, 彭成, 游万海, 邓超

湖南大学工商管理学院, 湖南长沙 410082

收稿日期:2013-07-16 修回日期:2014-02-19 出版日期:2014-07-20 发布日期:2014-07-24
作者简介:朱慧明（1966-），男（汉族），湖南湘潭人，湖南工商大学管理学院，教授，博士生导师;研究方向：贝叶斯计量经济模型。
基金资助:
国家自然科学基金项目（71221001，71031004，7171075）；教育部博士点基金项目（20110161110025）；湖南省自然科学基金项目（11JJ3090）

The Dynamic Dependence Between Crude Oil Market and Stock Market Based on Bayesian Wishart Multivariate Stochastic Volatility model

ZHU Hui-ming, PENG Cheng, YOU Wan-hai, DENG Chao

College of Business Administration, Hunan University, Changsha 410082, China

Received:2013-07-16 Revised:2014-02-19 Online:2014-07-20 Published:2014-07-24

摘要/Abstract

摘要： 针对时变相关系数矩阵在多变量随机波动模型的估计问题，构建了贝叶斯动态相关Wishart波动模型。在CC-MSV模型的基础上，设置精度矩阵服从Wishart分布，使得模型的相关系数矩阵具有时变特征。通过模型的统计结构分析，选择参数先验分布，设计相应的Gibbs-MTM-ARMS混合算法，据此估计模型参数；并利用上证综合指数、标普500指数与原油期货价格数据进行实证分析。研究结果表明：模型能够有效地刻画原油市场与股票市场的动态相依性；金融危机期间，股票市场与原油市场的相关性较强，并且难以判断正负方向；金融危机后，中国股票市场与原油市场呈现极微弱的相关性，而美国股票市场与原油市场的正相关性较为明显。

关键词: 动态相依性, 随机波动, 贝叶斯分析, Wishart分布, Gibbs-MTM-ARMS混合算法

Abstract: Bayesian dynamic correlation Wishart volatility model is established in this paper to address estimation problem of time-varying coefficient matrix in multivariate stochastic volatility. In order to make the correlation coefficient matrix incorporated time-varying characteristics, the precision matrices in CC-MSV models are set to following the Wishart distribution. Based on the analysis of statistic structure of model and the selection of parameters prior, the Gibss-MTM-ARMS sampling algorithm method is utilized to estimate model parameters. The empirical research applies the data of Shanghai Composite Index, S&P500 and crude oil future price. The research results show that the correlation between crude oil market and stock market is strong during the financial crisis, but it is difficult to identify the direction. Further, after the financial crisis, the U.S. stock market is obviously positive correlated with crude oil market, while the correlation between China stock market and crude oil market is very weak. All those indicate that models used in this paper can effectively depict the dynamic dependency between crude oil market and stock market.

Key words: dynamic dependence, stochastic volatility, Bayesian analysis, Wishart distribution, Gibbs-MTM-ARMS algorithm

中图分类号:

F224

朱慧明, 彭成, 游万海, 邓超. 基于贝叶斯Wishart波动模型的原油市场与股市动态相依性研究[J]. 中国管理科学, 2014, 22(7): 1-9.

ZHU Hui-ming, PENG Cheng, YOU Wan-hai, DENG Chao. The Dynamic Dependence Between Crude Oil Market and Stock Market Based on Bayesian Wishart Multivariate Stochastic Volatility model[J]. Chinese Journal of Management Science, 2014, 22(7): 1-9.

参考文献

[1] Kilian L, Park C. The impact of oil price shocks on the US stock market [J]. International Economic Review, 2009, 50(4): 1267-1287.

[2] Wen Xiaoqian, Wei Yu, Huang Dengshi. Measuring contagion between energy market and stock market during financial crisis: A copula approach [J]. Energy Economics, 2012, 34(5): 1435-1446.

[3] Masih R, Peters S, De Mello L. Oil price volatility and stock price fluctuations in an emerging market: Evidence from South Korea [J]. Energy Economics, 2011, 33(5): 975-986.

[4] Arouri M E H, Rault C. Oil prices and stock markets in GCC countries: Empirical evidence from panel analysis [J]. International Journal of Finance & Economics, 2012, 17(3): 242-253.

[5] Nguyen C C, Bhatti M I. Copula model dependency between oil prices and stock markets: Evidence from China and Vietnam [J]. Journal of International Financial Markets, Institutions and Money, 2012, 22(4): 758-773.

[6] Kilian L. Exogenous oil supply shocks: How big are they and how much do they matter for the US economy? [J]. The Review of Economics and Statistics, 2008, 90(2): 216-240.

[7] 刘红忠,何文忠,李治平. A股市场上得"中石油魔咒"现象及其解释 [J]. 财经研究, 2012, 38(8): 109-121.

[8] Park J, Ratti R A. Oil price shocks and stock markets in the US and 13 European countries [J]. Energy Economics, 2008, 30(5): 2587-2608.

[9] Miller J I, Ratti R A. Crude oil and stock markets: Stability, instability, and bubbles [J]. Energy Economics, 2009, 31(4): 559-568.

[10] 姬强,范英. 次贷危机前后国际原油市场与中美股票市场间的协动性研究 [J]. 中国管理科学, 2010, 18(6): 42-50.

[11] Vo M. Oil and stock market volatility: A multivariate stochastic volatility perspective [J]. Energy Economics, 2011, 33(5): 956-965.

[12] Philipov A, Glickman M E. Multivariate stochastic volatility via Wishart processes [J]. Journal of Business & Economic Statistics, 2006, 24(3): 313-328.

[13] Asai M, McAleer M. The structure of dynamic correlations in multivariate stochastic volatility models [J]. Journal of Econometrics, 2009, 150(2): 182-192.

[14] Bauwens L, Laurent S, Rombouts J V K. Multivariate GARCH models: A survey [J]. Journal of applied econometrics, 2006, 21(1): 79-109.

[15] Asai M, McAleer M, Yu Jun. Multivariate stochastic volatility: A review [J]. Econometric Reviews, 2006, 25(2-3): 145-175.

[16] Gouriéroux C, Jasiak J, Sufana R. The Wishart autoregressive process of multivariate stochastic volatility [J]. Journal of Econometrics, 2009, 150(2): 167-181.

[17] Golosnoy V, Gribisch B, Liesenfeld R. The conditional autoregressive Wishart model for multivariate stock market volatility [J]. Journal of Econometrics, 2012, 167(1): 211-223.

[18] Jin Xin, Maheu J M. Modeling realized covariances and returns [J]. Journal of Financial Econometrics, 2013, 11(2): 335-369.

[19] Bauwens L, Laurent S, Rombouts J V K. Multivariate GARCH models: A survey[J]. Journal of applied econometrics, 2006, 21(1): 79-109.

[20] Engle R. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models [J]. Journal of Business & Economic Statistics, 2002, 20(3): 339-350.

基于贝叶斯Wishart波动模型的原油市场与股市动态相依性研究

The Dynamic Dependence Between Crude Oil Market and Stock Market Based on Bayesian Wishart Multivariate Stochastic Volatility model

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 13

Metrics

本文评价

推荐阅读 10

[1]	吴鑫育, 赵凯, 李心丹, 马超群. 时变风险厌恶下的期权定价——基于上证50ETF期权的实证研究[J]. 中国管理科学, 2019, 27(11): 11-22.
[2]	吴鑫育, 李心丹, 马超群. 门限已实现随机波动率模型及其实证研究[J]. 中国管理科学, 2017, 25(3): 10-19.
[3]	周艳丽, 吴洋, 葛翔宇. 一类高新技术企业专利权价值的实物期权评估方法——基于跳扩散过程和随机波动率的美式期权的建模与模拟[J]. 中国管理科学, 2016, 24(6): 19-28.
[4]	李素芳, 朱慧明, 李荣. 基于贝叶斯机制转换协整模型的石油——股市非对称效应研究[J]. 中国管理科学, 2015, 23(9): 46-54.
[5]	肖建武, 尹希明. 待遇预定制养老基金资产组合与缴费计划最优决策——基于随机波动率Heston模型及Legendre对偶变换法[J]. 中国管理科学, 2015, 23(3): 42-46.
[6]	吴鑫育, 杨文昱, 马超群, 汪寿阳. 基于非仿射随机波动率模型的期权定价研究[J]. 中国管理科学, 2013, (1): 1-7.
[7]	吴鑫育, 周海林, 马超群, 汪寿阳. 基于随机贴现因子方法的权证定价研究[J]. 中国管理科学, 2012, (4): 1-7.
[8]	朱慧明, 黄超, 郝立亚, 虞克明, 李素芳. 基于状态空间的贝叶斯跳跃厚尾金融随机波动模型研究[J]. 中国管理科学, 2010, 18(6): 17-25.
[9]	吴吉林, 陶旺升. 基于机制转换与随机波动的我国短期利率研究[J]. 中国管理科学, 2009, 17(3): 40-46.
[10]	朱微亮, 刘海龙. 稳健的动态资产组合模型研究[J]. 中国管理科学, 2007, 15(3): 19-24.
[11]	林静, 韩玉启, 朱慧明. 基于MCMC稳态模拟的贝叶斯经验费率厘定信用模型[J]. 中国管理科学, 2006, (2): 33-38.
[12]	陈金亮, 徐渝, 贾涛. 对称信息下具有需求预测更新的供应链协调模型分析[J]. 中国管理科学, 2005, (1): 37-41.
[13]	沈根祥. 股票收益随机波动模型研究[J]. 中国管理科学, 2003, (2): 16-20.