The oil market and stock market are the important part of modern economy, playing an important role in the economy. The relationship between these markets plays a key role in analyzing the fluctuation of price and risk transmission. The vine copula model is used in this paper to research the dependence relationship is used among the oil price, Chinese stock price and American stock price, and then the dependence relationship to manage risk.The vine copula model is used to model the dependence relationship of the oil price and ten industrial stock prices in China and American respectively,therefore the dependence relationship and dependence structure is estimated. And then oil price and the industrial stock price that has stronger dependence relationship with oil price are selected to construct the portfolio and meantime measure the risk of portfolio.The research findings of this paper show the dependence relationships of oil price, Chinese and American stock prices are different in industry, the dependence relationship between China stock price and oil price is weaker than the dependence relationship between American stock price and oil price.At the same time, dependence relationships are also used to establish two portfolios and estimate their risk,empirical results find that the vine copula model has a better performance in estimating the risk of portfolio which is established by stronger dependence relationship. The research will expand the application of the vine copula model and the risk measurement using vine copula.
YU Le-an, ZHA Rui, HE Kai-jian, TANG Ling
. The Analysis of Dependence Relationship between Oil and Stock Prices: Evidence from China and American Industrial Sector Indices[J]. Chinese Journal of Management Science, 2018
, 26(11)
: 74
-82
.
DOI: 10.16381/j.cnki.issn1003-207x.2018.11.008
[1] 金洪飞, 金荦. 国际石油价格对中国股票市场的影响——基于行业数据的经验分析[J]. 金融研究, 2010,(2):173-187.
[2] 李红霞, 傅强. 能源价格冲击, 宏观经济因素与行业股价决定——来自中国上市公司28个行业板块的经验证据[J]. 山西财经大学学报, 2011,(6):11-19.
[3] 郭名媛, 王娜. 原油价格变动对中国基础工业收益的影响——基于格兰杰因果关系检验的实证研究[J]. 北京理工大学学报:社会科学版, 2015,(4):18-27.
[4] 程安, 常清. 油价与我国石油公司股价间的动态关系研究[J]. 华南理工大学学报:社会科学版, 2016, 18(1):1-11.
[5] Elyasiani E, Mansur I, Odusami B. Oil price shocks and industry stock returns[J]. Energy Economics, 2011, 33(5):966-974.
[6] Sadorsky P. Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies[J]. Energy Economics, 2012, 34(1):248-255.
[7] Arouri M E H, Jouini J, Nguyen D K. Volatility spillovers between oil prices and stock sector returns:Implications for portfolio management[J]. Journal of International Money & Finance, 2011, 30(7):1387-1405.
[8] Degiannakis S, Filis G, Floros C. Oil and stock returns:Evidence from European industrial sector indices in a time-varying environment[J]. Journal of International Financial Markets Institutions & Money, 2013, 26(26):175-191.
[9] Hamma W, Jarboui A, Ghorbel A. Effect of oil price volatility on Tunisian stock market at sector-level and effectiveness of hedging strategy[J]. Procedia Economics & Finance, 2014, 13:109-127.
[10] Zhang Bangzheng, Wei Yu, Yu Jiang, et al. Forecasting VaR and ES of stock index portfolio:A Vine copula method[J]. Physica A Statistical Mechanics & Its Applications, 2014, 416:112-124.
[11] Zhang Dalu. Vine copulas and applications to the European Union sovereign debt analysis[J]. International Review of Financial Analysis, 2014, 36:46-56.
[12] Joe H, Li Haijun, Nikoloulopoulos A K. Tail dependence functions and vine copulas[J]. Journal of Multivariate Analysis, 2010, 101(1):252-270.
[13] Righi M B, Ceretta P S. Analyzing the dependence structure of various sectors in the Brazilian market:A Pair Copula Construction approach[J]. Economic Modelling, 2013, 35(5):199-206.
[14] Beatriz V D M M, Bello A V. Robust pair-copula based forecasts of realized volatility[J]. Applied Stochastic Models in Business & Industry, 2014, 30(2):183-199.
[15] Czado C, Min A. Bayesian inference for D-Vines:Estimation and model selection[M].Dependence Modeling:Hackensack,NJ:World Sci. Publ.,2011:249-264.
[16] Smith M S. Copula modelling of dependence in multivariate time series[J]. International Journal of Forecasting, 2015, 31(3):815-833.
[17] 杨坤, 于文华, 魏宇. 基于R-vine copula的原油市场极端风险动态测度研究[J]. 中国管理科学, 2017, 25(8):19-29.
[18] Sukcharoen K, Leatham D J. Hedging downside risk of oil refineries:A vine copula Approach[J]. Energy Economics, 2017, 66:493-507.
[19] Bensaïda A.The contagion effect in European sovereign debt markets:A regime-switching vine copula approach[J]. International Review of Financial Analysis, 2017, 58:153-165.
[20] 韩超, 严太华. 基于高维动态藤Copula的汇率组合风险分析[J]. 中国管理科学, 2017, 25(2):10-20.
[21] Sklar M.Fonctions de repartition an dimensions et leurs marges[J]. Publ. Inst. Statist. Univ Paris, 1959,8:229-231.
[22] Joe H. Multivariate Models and Multivariate Dependence Concepts[M]. London:Chapman & Hall/CRC, 1997.
