基于MSV与CoVaR模型的公司债市场与股票市场间风险溢出效应研究

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英文篇名：Risk Spillover Effect Between Corporate Bond Market and Stock Market Based on MSV and CoVaR Model 作者：曾志坚 ; 张欣怡 ; 左楠 英文作者：ZENG Zhijian;ZHANG Xinyi;ZUO Nan;Business School, Hunan University; 关键词：风险溢出 ; 公司债市场 ; 股票市场 ; GC-t-MSV模型 ; CoVaR模型 英文关键词：risk spillover;;corporate bond market;;stock market;;GC-t-MSV model;;CoVaR model 中文刊名：CLSJ 英文刊名：The Theory and Practice of Finance and Economics 机构：湖南大学工商管理学院; 出版日期：2019-03-25 出版单位：财经理论与实践 年：2019 期：v.40;No.218 基金：国家自然科学基金(71340014);; 湖南省自然科学基金(2018JJ2068) 语种：中文; 页：CLSJ201902006 页数：7 CN：02 ISSN：43-1057/F 分类号：44-50

摘要

依据2015—2017年中证公司债指数与沪深300指数的日收益数据,运用GC-t-MSV模型,检验中国公司债市场与股票市场间的风险溢出效应,并通过条件在险价值(CoVaR)模型度量两市场间风险溢出效应。结果表明:公司债市场与股票市场间存在不对称的双向风险溢出效应,且公司债市场对股票市场的风险溢出效应强于股票市场对公司债市场的风险溢出效应;公司债市场与股票市场的波动受其自身波动的影响较大,鉴此,监管部门和投资者应增强对公司债市场的关注,根据公司债市场的风险变化及时采取应对措施,充分发挥其风险信号作用。
        Based on the daily yield data of the CSI Bond index and CSI 300 index from 2015 to 2017, the author used GC-t-MSV model to test the risk spillover effect between the corporate bond market and the stock market in China, and measured the intensity of risk spillover between the two markets through the conditional value at risk(CoVaR.) The empirical results show that there is a bi-directional risk spillover effect between the corporate bond market and the stock market, and the risk spillover effect of the corporate bond market on the stock market is stronger than the risk spillover effect of the stock market on the corporate bond market. In addition, it is also found that the volatility of the corporate bond market and the stock market is greatly affected by its own volatility.

引文

[1] Roni B, Abbas G, Wang S. Return and volatility spillovers effects:study of asian emerging stock markets[J]. Journal of Systems Science and Information, 2018, 6(2): 97-119.
    [2] Wang L. Study on the extreme risk spillover between China and world stock market after China’s share structure reform[J]. Journal of Financial Risk Management, 2014, 3(2): 50-56.
    [3] Wang Y, Pan Z, Wu C. Volatility spillover from the US to international stock markets: a heterogeneous volatility spillover GARCH model[J]. Journal of Forecasting, 2018, 37(3): 385-400.
    [4] Chi W, Brooks R D, Bissoondoyal-Bheenick E. Volatility spillover between the US, Chinese and australian stock market[J]. Social Science Electronic Publishing, 2018, 43(2): 263-285.
    [5] 谢家泉. 股灾背景下中美股市风险溢价的结构转换研究[J]. 运筹与管理, 2017, 26(2): 127-134.
    [6] 董艳, 梁满发. 基于GC-MSV模型的国内外股市波动溢出效应分析[J]. 财会月刊, 2013(10): 17-20.
    [7] 陈学彬, 曾裕峰. 中美股票市场和债券市场联动效应的比较研究——基于尾部风险溢出的视角[J]. 经济管理, 2016, 38(7): 1-13.
    [8] 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.
    [9] 史永东, 丁伟, 袁绍锋. 市场互联、风险溢出与金融稳定——基于股票市场与债券市场溢出效应分析的视角[J]. 金融研究, 2013(3): 170-180.
    [10] 左正强. 股票市场与债券市场之间风险外溢效应研究[J]. 统计与决策, 2014(10): 155-157.
    [11] 王琳, 沈沛龙. 基于DCC-GARCH模型的中国上市银行系统性风险研究[J]. 财经理论与实践, 2017,38(1): 24-29.
    [12] 潘凌遥, 蒋晓泉, 费紫微. 中国系统重要性银行附加资本计提机制研究——基于Copula-CoVaR模型[J]. 财经理论与实践, 2015, 36(3): 23-28.
    [13] 熊正德, 韩丽君. 金融市场间波动溢出效应研究——GC-MSV模型及其应用[J]. 中国管理科学, 2013, 21(2): 32-41.
    [14] Yu J, Meyer R. Multivariate stochastic volatility models: bayesian estimation and model comparison[J]. Econometric Reviews, 2006, 25(2): 361-384.
    [15] Adrian T, Brunnermeier M K. CoVaR[J].American Economic Review, 2016,106(7):1705-1741.