基于R-vine copula的原油市场极端风险动态测度研究

doi:10.16381/j.cnki.issn1003-207x.2017.08.003

中国管理科学 ›› 2017, Vol. 25 ›› Issue (8): 19-29.doi: 10.16381/j.cnki.issn1003-207x.2017.08.003

基于R-vine copula的原油市场极端风险动态测度研究

杨坤¹, 于文华¹, 魏宇^2,3

1. 成都理工大学商学院, 四川成都 610059;
2. 云南财经大学金融学院, 云南昆明 650221;
3. 西南交通大学经济管理学院, 四川成都 610031

收稿日期:2016-09-26 修回日期:2016-12-08 出版日期:2017-08-20 发布日期:2017-10-16
通讯作者: 魏宇(1975-),男(汉族),四川攀枝花人,云南财经大学金融学院教授,博士生导师,研究方向:金融与能源市场风险管理,E-mail:weiyusy@126.com. E-mail:weiyusy@126.com
基金资助:
国家自然科学基金资助项目（71371157，71671145）；教育部人文社科基金规划项目（15YJA790031，16YJA790062，17YJA790015，17XJA790002）；中央高校基本科研业务费专项资金资助项目（26816WCX02）；四川省科技青年基金项目（2015JQO010）；四川省教育厅人文社科重点项目（14SA0039）；成都理工大学中青年骨干教师培养计划资助项目（JXGG201420）；国家级大学生创新创业训练计划项目（201610616035）

Dynamic Measurement of Extreme Risk among Various Crude Oil Markets Based on R-vine copula

YANG Kun¹, YU Wen-hua¹, WEI Yu^2,3

1. Commercial College, Chengdu University of Technology, Chengdu, 610059, China;
2. School of Finance, Yunnan University of Finance and Econamics, Kunming 650021, China;
3. School of Economics and Management, Southwest Jiaotong University, Chengdu, 610031, China

Received:2016-09-26 Revised:2016-12-08 Online:2017-08-20 Published:2017-10-16

摘要/Abstract

摘要： 近年来，原油价格的暴涨暴跌给实体经济的稳定发展带来了众多的不确定因素，因此对原油市场的极端波动风险进行准确刻画和预测具有重要的理论和现实意义。结合EVT极值理论，构建五类R-vine copula模型，刻画了六大原油市场间的极值风险相依关系，在此基础上，分别构建资产组合的在险价值（VaR）与预期损失（ES）模型，进行样本外极端风险的滚动测度，并通过backtesting方法，对比了各类模型测度精度的差异状况。研究结果表明：结合EVT极值理论的Mixed R-vine copula模型能够有效地描述原油市场间的尾部极值风险相依关系，取得了更好的风险测度效果；VaR模型能够较好地测度较低风险水平上的组合风险价值，但在高风险水平上的测度效果却有所不足，而ES模型则在高风险水平上表现出了更为优异的组合风险测度能力。

关键词: 原油市场, R-vine copula, 极值理论, 在险价值(VaR), 预期损失(ES), Backtesting

Abstract: In recent years, the strong fluctuations in crude oil prices bring many uncertain factors to the stable development of real economy, so there is an important theoretical and practical significance in accurately characterizing and predicting the extreme volatility riskamong various crude oil markets. In this paper, combining extreme value theory(EVT) with fivecategories of R-vine copula models, the extreme dependence relationship between six crude oil markets is depicted. Based on that the value at risk(VaR) and expected shortfall(ES) models are constructed to measure the out-of-sample extreme risk using a sliding time window method. Finally, a backtesting for unconditional coverage and backtesting based on bootstrap are, and carried out the VaR and ES measurement accuracy of different models is compared. The empirical results are summarized as follows:(1) Mixed R-vine-EVT model can describe the extreme dependence relationshipamong various crude oil marketsmore excellent and show a better risk measures efficiency.(2) VaR model can well depict the riskstatusat low risk levels, while the measure precision at high risk levels is insufficient. On the contrary, ES model shows a better risk measurement capability at the high risk levels.Accordingly, some practical suggestions are put forward e.g., investors should introduceextreme value theory to describe the extreme risk situation of crude oil markets; under the background of sharp fluctuations in international crude oil prices, Mixed R-vine model can more adapt to the changes of dependency relationship among various crude oil markets.

Key words: crude oil market, R-vine copula, extreme value theory, value at risk, expected shortfall, backtesting

中图分类号:

F830
F224

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.

YANG Kun, YU Wen-hua, WEI Yu. Dynamic Measurement of Extreme Risk among Various Crude Oil Markets Based on R-vine copula[J]. Chinese Journal of Management Science, 2017, 25(8): 19-29.

参考文献

[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] Zhang Yuejun,Zhang Lu. Interpreting the crude oil price movements:Evidence from the Markov regime switching model[J]. Applied Energy,2015,143:96-109.

[3] Moya-Martínez P,Ferrer-Lapeña R,Escribano-Sotos F. Oil price risk in the Spanish stock market:An industry perspective[J]. Economic Modelling,2014,37:280-290.

[4] 王雪标,周维利,范庆珍.我国原油价格与外国原油价格的波动溢出效应——基于DCC-MGARCH模型分析[J].数理统计与管理,2012,31(4):571-584.

[5] Regnier E. Oil and energy price volatility[J]. Energy Economics,2007,29(3):405-427.

[6] 潘伟,王凤侠,吴婷.不同突发事件下进口原油采购策略[J].中国管理科学,2016,24(7):27-35.

[7] 马锋,魏宇,黄登仕.基于vine copula方法的股市组合动态VaR测度及预测模型研究[J].系统工程理论实践,2015,35(1):26-36.

[8] 于文华,魏宇,康明惠.不同时变Copula-EVT-ES模型精度比较研究[J].管理科学学报,2015,18(5):32-45.

[9] Artzner P,Delbaen F,Eber J M,et al. Coherent Measures of Risk[J]. Mathematical Finance,1999,9(3),203-228.

[10] 王鹏.基于时变高阶矩波动模型的VaR与ES度量[J].管理科学学报,2013,16(2):33-45.

[11] 刘明磊,姬强,范英.金融危机前后国内外石油市场风险传导机制研究[J].数理统计与管理,2014,33(1):9-20.

[12] Fan Ying,Zhang Yuejun,Tsai H T,et al. Estimating ‘Value at Risk’ of crude oil price and its spillover effect using the GED-GARCH approach[J]. Energy Economics,2008,30(6):3156-3171.

[13] 王鹏,魏宇.经典金融理论的困境与金融物理学研究的兴起[J].管理科学学报,2014,17(9):40-55.

[14] 张帮正,魏宇,余江,等.基于EVT-Vine-copula的多市场相关性及投资组合选择研究[J].管理科学,2014,27(3):133-144.

[15] 鲁志军,姚德权.基于Copula-VaR的金融资产组合风险测度研究[J].财经理论与实践,2012,33(180):48-52.

[16] 于文华,魏宇,淳伟德.欧债危机环境中资产组合ES模型比较研究[J].中国管理科学,2014,22(5):8-15.

[17] 高江.藤Copula模型与多资产投资组合VaR预测[J].数理统计与管理,2013,32(2):247-258.

[18] Joe H. Families of m-variate distributions with given margins and m(m-1)/2 bivariate dependence parameters[J]. Lecture Notes-Monograph Series,1996,28:120-141.

[19] Bedford T,Cooke R M. Probability density decomposition for conditionally dependent random variables modeled by vines[J]. Annals of Mathematics and Artificial intelligence,2001,32(1-4):245-268.

[20] Bedford T,Cooke R M. Vines-A new graphical model for dependent random variables[J]. Annals of Statistics,2002,30(4):1031-1068.

[21] Aas K,Czado C,Frigessi A,et al. Pair-copula constructions of multiple dependence[J]. Insurance:Mathematics and economics,2009,44(2):182-198.

[22] Koliai L. Extreme risk modelling:An EVT-pair-copulas approach for financial stress tests[J]. Journal of Banking & Finance,2016,70:1-22.

[23] Jondeau E,Rockinger M. Conditional dependency of financial series:The Copula-GARCH model[J]. Journal of International Money and Finance,2006,25(5):827-853.

[24] 魏宇.股票市场的极值风险测度及后验分析研究[J].管理科学学报,2008,11(1):78-88.

[25] Ayusuk A,Sriboonchitta S. Risk analysis in asian emerging markets using Canonical vine copula and extreme value theory[J]. Thai Journal of Mathematics,2014,(SI):59-72.

[26] 林宇,陈王,王一鸣,等.典型事实、混合Copula函数与金融市场相依结构研究[J].中国管理科学,2015,23(4):20-29.

[27] Pickands Ⅲ J. Statistical inference using extreme order statistics[J]. The Annals of Statistics,1975,3(1):119-131.

[28] Balkema A A,De Haan L. Residual life time at great age[J]. Annals of Probability,1974,2(5):792-804.

[29] Dißmann J. Statistical inference for regular vines and application[D]. München:Technische Universitat München,2010.

[30] Brechmann E C. Truncated and simplified regular vines and their applications[D]. Müuchen:Technische Universitat Miinchen,2010.

[31] Vuong Q H. Likelihood ratio tests for model selection and non-nested hypotheses[J]. Econometrica,1989,57(2):307-333.

[32] Kupiec P. Techniques for verifying the accuracy of risk measurement models[J]. Journal of Derivatives,1995,2:173-184.

[33] McNeil A,Frey R. Estimation of tail-related risk measures for heteroscedastic financial time series:An extreme value approach[J]. Journal of Empirical Finance,2000,7(3):271-300.

[34] DuMouchel W H. Estimating the stable index α in order to measure tail thickness:A critique[J]. The Annals of Statistics,1983,11(4):1019-1031.

[35] Lu Fengbin,Hong Yongmiao,Wang Shouyang,et al. Time-varying Granger causality tests for applications in global crude oil markets[J]. Energy Economics,2014,42(1):289-298.

基于R-vine copula的原油市场极端风险动态测度研究

Dynamic Measurement of Extreme Risk among Various Crude Oil Markets Based on R-vine copula

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

Metrics

本文评价

推荐阅读 10

[1]	唐振鹏, 吴俊传, 冉梦, 张婷婷. 考虑投资者情绪的中国股市自激发效应研究[J]. 中国管理科学, 2020, 28(7): 1-12.
[2]	李红权, 何敏园, 黄莹莹. 我国金融机构的系统重要性评估:基于多元极值理论[J]. 中国管理科学, 2020, 28(5): 14-24.
[3]	朱鹏飞, 唐勇, 钟莉. 基于小波-高阶矩模型的投资组合策略——以国际原油市场为例[J]. 中国管理科学, 2020, 28(10): 24-35.
[4]	刘炳越, 姬强, 范英. 黄金是否为原油的“避险天堂”?——基于组合收益及其波动视角[J]. 中国管理科学, 2018, 26(11): 1-10.
[5]	张晨, 丁洋, 汪文隽. 国际碳市场风险价值度量的新方法——基于EVT-CAViaR模型[J]. 中国管理科学, 2015, 23(11): 12-20.
[6]	姬新龙, 周孝华. 基于马尔科夫随机波动和极值理论的风险测度[J]. 中国管理科学, 2014, 22(10): 44-51.
[7]	陆静, 张佳. 基于极值理论和多元Copula函数的商业银行操作风险计量研究[J]. 中国管理科学, 2013, 21(3): 11-19.
[8]	魏宇 . 基于多分形理论的动态VaR预测模型研究[J]. 中国管理科学, 2012, 20(5): 7-15.
[9]	王艺馨, 周勇. 极端情况下对我国股市风险的实证研究 [J]. 中国管理科学, 2012, 20(3): 20-27.
[10]	薛勇, 郭菊娥, 薛冬. 期货市场涨跌停板幅度设置的模型研究[J]. 中国管理科学, 2010, 18(6): 9-16.
[11]	叶五一, 缪柏其. 应用复合极值理论估计动态流动性调整VaR[J]. 中国管理科学, 2008, 16(3): 44-49.