中国金融业不同板块间风险传导的非对称性研究——基于非对称MVMQ-CAViaR模型的实证分析

doi:10.16381/j.cnki.issn1003-207x.2017.08.007

中国管理科学 ›› 2017, Vol. 25 ›› Issue (8): 58-67.doi: 10.16381/j.cnki.issn1003-207x.2017.08.007

中国金融业不同板块间风险传导的非对称性研究——基于非对称MVMQ-CAViaR模型的实证分析

曾裕峰¹, 简志宏², 彭伟³

1. 复旦大学经济学学院, 上海 200433;
2. 华中科技大学经济学院, 湖北武汉 430074;
3. 中南财经政法大学, 湖北武汉 430073

收稿日期:2016-02-27 修回日期:2017-03-07 出版日期:2017-08-20 发布日期:2017-10-16
通讯作者: 简志宏(1968-),男(汉族),四川泸州人,华中科技大学经济学院教授,博士生导师,研究方向:金融数学、宏观金融,E-mail:jian8822@sina.com. E-mail:jian8822@sina.com
基金资助:
中央高校基本科研业务费资助项目（2016AD007）；国家自然科学基金资助项目（71402191）

Study on Asymmetric Effect of Risk Transmission between Different Financial Sectors in China

ZENG Yu-feng¹, JIAN Zhi-hong², PENG Wei³

1. School of Economics, Fudan University, Shanghai 200433, China;
2. School of Economics HUST, Wuhan 430074, China;
3. School of Finance, Zhongnan University of Economics and Law, Wuhan 430073, China

Received:2016-02-27 Revised:2017-03-07 Online:2017-08-20 Published:2017-10-16

摘要/Abstract

摘要： 针对原始MVMQ-CAViaR模型未考虑正负冲击具有非对称性的不足，本文将其扩展为非对称MVMQ-CAViaR模型和联合非对称MVMQ-CAViaR模型，继而运用该模型分析了我国金融业不同板块间的风险传导效应，并采用严谨的后测检验对比了各个模型的预测效果。结果表明，银行对证券和保险板块均具有显著的风险传染效应，而证券只能单方向地吸收其他板块的风险溢出；正负信息冲击对自身以及其他板块存在不同程度的非对称特征，且指数下跌对VaR的影响效果要强于指数上涨，联合负向冲击会放大原有的风险水平；新构建的两个非对称模型能显著提升原有模型风险预测精度，其中联合非对称MVMQ-CAViaR模型的预测效果更佳。

关键词: 风险传导, MVMQ-CAViaR模型, 非对称性, 系统性风险

Abstract: Ever since the 2008 global financial crisis, the supervision of systemic financial risk has been a hot topic in the field of academic and policy-making departments, both at home and abroad. Especially since 2012, financial system reform began to accelerate, and investment constrain have been gradually deregulated. The extensive relevance and intersectionality of the financial services business brought about significant changes in the financial sector, which led to a substantial increase in systemic financial risk.The multivariate quantile regression model provides a good tool for analyzing systemic risk. Considering the deficiency of original MVMQ-CAViaR model ignores the asymmetric impacts of positive and negative shock. In this paper, it is extended to asymmetric MVMQ-CAViaR model and joint asymmetric MVMQ-CAViaR model. Subsequently, these models are used to study China's financial industry risk transmission effect between different sectors. Then both Kupiec LR(likelihood ratio) test and dynamic quantile test are used to backtest the prediction performance of these models.
The results show that:Banks have significant spillover effects on securities and insurance sectors, while securities can just unidirectional absorb other sectors' risk;The impacts of good and bad news exhibit leverage effect to some extent to their own as well as other sectors. In general, negative shock has greater effect than positive effect. Furthermore, joint negative impact will amplify the current risk level;Two newly constructed models can significantly improve the risk prediction accuracy, and joint asymmetric MVMQ-CAViaR model is relatively more competitive.
Important practical and social implication are suggested.First of all. Regulators should pay special attention on strengthening the disclosure system of bank risk and the transparency of bank financial information. Then policy makers should strengthen the macro-prudential regulatory requirements and build good co-operation relationship between different industries in order to deal with emergency warning system.

Key words: risk transmission, MVMQ-CAViaR model, asymmetric characteristics, systemic risk

中图分类号:

F830.91

曾裕峰, 简志宏, 彭伟. 中国金融业不同板块间风险传导的非对称性研究——基于非对称MVMQ-CAViaR模型的实证分析[J]. 中国管理科学, 2017, 25(8): 58-67.

ZENG Yu-feng, JIAN Zhi-hong, PENG Wei. Study on Asymmetric Effect of Risk Transmission between Different Financial Sectors in China[J]. Chinese Journal of Management Science, 2017, 25(8): 58-67.

参考文献

[1] Kaufman G G, Scott K E. What is systemic risk, and do bank regulators retard or contribute to it?[J]. Independent Review, 2003, 7(3):371-391.

[2] Kupiec P, Nickerson D. Assessing systemic risk exposure from banks and GSEs under alternative approaches to capital regulation[J]. The Journal of Real Estate Finance and Economics, 2004, 28(2-3):123-145.

[3] 陈建青,王擎,许韶辉. 金融行业间的系统性金融风险溢出效应研究[J]. 数量经济技术经济研究,2015, (9):89-100.

[4] Forbes K J, Rigobon R. No contagion, only interdependence:measuring stock market comovements[J]. The Journal of Finance, 2002, 57(5):2223-2261.

[5] Li Xiaoming, Zou Liping. How do policy and information shocks impact co-movements of China's T-bond and stock markets?[J]. Journal of Banking & Finance, 2008, 32(3):347-359.

[6] Lee C H, Doong S C, Chou P I. Dynamic correlation between stock prices and exchange rates[J]. Applied financial economics, 2011, 21(11):789-800.

[7] 胡秋灵,马丽.我国股票市场和债券市场波动溢出效应分析[J]. 金融研究,2011,(10):198-206.

[8] 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.

[9] 贾彦东. 金融机构的系统重要性分析——金融网络中的系统风险衡量与成本分担[J]. 金融研究,2011,(10):17-33.

[10] 欧阳红兵,刘晓东. 中国金融机构的系统重要性及系统性风险传染机制分析——基于复杂网络的视角[J]. 中国管理科学,2015,23(10):30-37.

[11] Billio M, Getmansky M, Lo A W, et al. Econometric measures of connectedness and systemic risk in the finance and insurance sectors[J]. Journal of Financial Economics, 2012, 104(3):535-559.

[12] White H, Kim T H, Manganelli S. VAR for VaR:Measuring tail dependence using multivariate regression quantiles[J]. Journal of Econometrics, 2015, 187(1):169-188.

[13] 刘庆富,周程远.中国股票市场的非对称效应研究[J].系统工程学报,2012,27(5):648-655.

[14] Beber A, Brandt M W. When it cannot get better or worse:The asymmetric impact of good and bad news on bond returns in expansions and recessions[J]. Review of Finance, 2010, 14(1):119-155.

[15] 陆蓉,徐龙炳."牛市"和"熊市"对信息的不平衡性反应研究[J]. 经济研究,2004,(3):65-72.

[16] 文凤华,刘晓群,唐海如,等. 基于LHAR-RV-V模型的中国股市波动性研究[J]. 管理科学学报,2012,15(6):59-67.

[17] Engle R F,Manganelli S. CAViaR:Conditional autoregressive value at risk by regression quantile[J]. Journal of Business and Economic Statistics, 2004, 22(4):367-381.

[18] López-Espinosa G, Moreno A, Rubia A, et al. Short-term wholesale funding and systemic risk:A global CoVaR approach[J]. Journal of Banking & Finance, 2012, 36(12):3150-3162.

[19] 简志宏,彭伟. 基于CAViaR模型的汇率隔夜风险研究[J].中国管理科学,2015,23(6):17-24.

[20] Kupiec P H. Techniques for verifying the accuracy of risk measurement models[J]. Journal of Derivatives, 1995, 3(2):73-84.

中国金融业不同板块间风险传导的非对称性研究——基于非对称MVMQ-CAViaR模型的实证分析

Study on Asymmetric Effect of Risk Transmission between Different Financial Sectors in China

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	吴文洋,蒋海,唐绅峰. 数字化转型、网络关联性与银行系统性风险[J]. 中国管理科学, 2024, 32(3): 9-19.
[2]	赵静, 郭晔. 金融强监管、影子银行与银行系统性风险[J]. 中国管理科学, 2023, 31(7): 50-59.
[3]	王虎, 李守伟, 马瑜寅, 刘晓星. 基于共同持股网络的基金系统性风险研究[J]. 中国管理科学, 2023, 31(6): 82-90.
[4]	欧阳资生, 杨希特, 黄颖. 嵌入网络舆情指数的中国金融机构系统性风险传染效应研究[J]. 中国管理科学, 2022, 30(4): 1-12.
[5]	刘志东, 张培元, 荆中博. 跨行业风险溢出冲击下我国银行业系统性风险研究[J]. 中国管理科学, 2022, 30(12): 1-12.
[6]	张飞鹏, 徐一雄, 邹胜轩, 陈艳. 基于LGCNET多层网络的中国A股上市公司系统性风险度量[J]. 中国管理科学, 2022, 30(12): 13-25.
[7]	高倩倩, 范宏. 基于银行-资产双边网络模型的系统性风险及投资策略研究[J]. 中国管理科学, 2021, 29(7): 1-12.
[8]	赵林海, 陈名智. 金融机构系统性风险溢出和系统性风险贡献——基于滚动窗口动态Copula模型双时变相依视角[J]. 中国管理科学, 2021, 29(7): 71-83.
[9]	王耀东, 冯燕, 周桦. 保险业在金融系统性风险传染路径中起到“媒介”作用吗？——基于金融市场尾部风险传染路径的实证分析[J]. 中国管理科学, 2021, 29(5): 14-24.
[10]	马钱挺, 杨文珂, 何建敏. 基于多层网络的银企系统性风险研究[J]. 中国管理科学, 2021, 29(12): 1-14.
[11]	张伟平, 庄新田, 王健. 中国股市跨行业系统性风险空间溢出关联及风险预测分析——基于尾部风险网络模型[J]. 中国管理科学, 2021, 29(12): 15-28.
[12]	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.
[13]	姜闪闪, 范宏. 双渠道风险传染下银行系统稳定性分析[J]. 中国管理科学, 2020, 28(11): 51-60.
[14]	刘超, 李元睿, 姜超, 马玉洁, 刘宸琦, 谢启伟. 中国证券公司系统性风险测度及演化特征研究——来自20家上市证券公司的数据[J]. 中国管理科学, 2019, 27(5): 11-22.
[15]	姚鸿, 王超, 何建敏, 李亮. 银行投资组合多元化与系统性风险的关系研究[J]. 中国管理科学, 2019, 27(2): 9-18.