主管:中国科学院
主办:中国优选法统筹法与经济数学研究会
   中国科学院科技战略咨询研究院

国际新兴资产与中国传统资产的多维溢出效应

  • 王雄 ,
  • 李景瑶 ,
  • 任晓航 ,
  • 王宗润
展开
  • 中南大学商学院,湖南 长沙 410083
任晓航(1991-),男(汉族),河北邯郸人,中南大学商学院,副教授,硕士生导师,博士,研究方向:金融计量与能源金融,E-mail:domrxh@outlook.com.

收稿日期: 2023-03-02

  修回日期: 2023-08-18

  网络出版日期: 2025-09-10

基金资助

国家自然科学基金青年项目(72403258);国家自然科学基金重大项目(72091515)

Multidimensional Spillover of International Emerging Assets and Chinese Traditional Assets: Based on the Quantile VAR Network

  • Xiong Wang ,
  • Jingyao Li ,
  • Xiaohang Ren ,
  • Zongrun Wang
Expand
  • School of Business,Central South University,Changsha 410083,China

Received date: 2023-03-02

  Revised date: 2023-08-18

  Online published: 2025-09-10

摘要

经济全球化背景下的金融市场风险呈现出跨地区溢出和交叉性传染的新特征。本文基于GARCHSK模型和分位数VAR网络框架深入考察了国际新兴资产(金融科技、比特币和绿色债券)与中国传统资产(股市、原油、商品和金属市场)之间在不同市场条件下的多维风险溢出。首先,静态溢出结果表明,资产间的溢出效应是非对称的,极端分位数处的溢出程度普遍高于中分位数,并且不同维度下的左右尾溢出效应也存在明显差异。其次,滚动窗口分析显示,资产价格的收益、波动、偏度和峰度溢出效应均呈现出显著的时变特征,并且这种溢出关系随时间变化的规律更多是受到外部冲击的驱动。最后,通过网络分析方法,发现各金融资产间定向溢出网络的结构特征、传染方向、作用强度和风险中心在不同矩层面和不同分位数下具有异质性。总之,本文的发现有利于进一步厘清国内外金融资产间的多维溢出关系和风险传染路径,对投资者构建多元化资产组合以及决策者加强宏观审慎监管具有深远意义。

本文引用格式

王雄 , 李景瑶 , 任晓航 , 王宗润 . 国际新兴资产与中国传统资产的多维溢出效应[J]. 中国管理科学, 2025 , 33(8) : 37 -49 . DOI: 10.16381/j.cnki.issn1003-207x.2023.0327

Abstract

Financial market risks in the context of economic globalization have taken on new characteristics of cross-regional spillovers and cross-contagion. An in-depth examination of multidimensional risk spillovers between international emerging assets (fintech, bitcoin and green bonds) and domestic traditional assets (stock market, crude oil, commodity and metal markets) under different market conditions is provided based on the GARCHSK model and the quantile VAR network framework. Firstly, the static results suggest that spillovers between assets are asymmetric, with spillovers generally higher at the extreme quartiles than at the middle quartile, and significant differences in left- and right-tail spillovers across dimensions. Secondly, rolling window analysis shows that asset price returns, volatility, skewness and kurtosis spillovers all exhibit significant time-varying characteristics, and that the pattern of such spillover relationships over time is more driven by external shocks. Finally, through network structure analysis, it is found heterogeneity in the structural characteristics, direction of contagion, intensity of action and risk centres of the targeted spillover network across financial assets at different moments and at different quantiles. Overall, the findings of this paper contribute to further understanding of the multidimensional spillover relationships and risk contagion paths of domestic and foreign financial assets, and have important implications for investors in constructing diversified asset portfolios and policymakers in strengthening macroprudential regulation.

参考文献

[1] 杨子晖, 陈里璇, 陈雨恬. 经济政策不确定性与系统性金融风险的跨市场传染——基于非线性网络关联的研究[J]. 经济研究202055(1): 65-81.
  Yang Z H, Chen L X, Chen Y T. Cross-market contagion of economic policy uncertainty and systemic financial risk: A nonlinear network connectedness analysis[J]. Economic Research Journal202055(1): 65-81.
[2] 刘少波, 张友泽, 梁晋恒. 金融科技与金融创新研究进展[J]. 经济学动态2021(3): 126-144.
  Liu S B, Zhang Y Z, Liang J H. Research progress on FinTech and financial innovation[J]. Economic Perspectives2021(3): 126-144.
[3] 姬强, 胡旻, 马嫣然, 等. 全球数字货币波动对中国金融资产的风险溢出效应研究[J]. 管理评论202234(2): 102-111.
  Ji Q, Hu M, Ma Y R, et al. Risk spillovers between global cryptocurrency and Chinese financial assets[J]. Management Review202234(2): 102-111.
[4] Monasterolo I, Raberto M. The EIRIN flow-of-funds behavioural model of green fiscal policies and green sovereign bonds[J]. Ecological Economics2018144: 228-243.
[5] Brunetti C, Harris J H, Mankad S, et al. Interconnectedness in the interbank market[J]. Journal of Financial Economics2019133(2): 520-538.
[6] 宫晓莉, 熊熊, 张维. 我国金融机构系统性风险度量与外溢效应研究[J]. 管理世界202036(8): 65-83.
  Gong X L, Xiong X, Zhang W. Research on systemic risk measurement and spillover effect of financial institutions in China[J]. Journal of Management World202036(8): 65-83.
[7] 杨子晖, 李东承, 王姝黛. 合成网络新视角下的输入性金融风险研究[J]. 中国工业经济2022(3): 38-56.
  Yang Z H, Li D C, Wang S D. Research on imported financial risk from the new perspective of composite network[J]. China Industrial Economics2022(3): 38-56.
[8] Da Fonseca J, Xu Y. Higher moment risk premiums for the crude oil market: A downside and upside conditional decomposition[J].Energy Economics201767: 410-422.
[9] 王鹏, 吕永健. 国际原油市场极端风险的测度模型及后验分析[J]. 金融研究2018(9): 192-206.
  Wang P, Lv Y J. Extreme risk measurement models of international oil market and backtesting analysis[J]. Journal of Financial Research2018(9): 192-206.
[10] 鲁万波, 黄光麟, Boudt Kris. 股市涨跌预测与量化投资策略: 基于时变矩成分分析[J]. 中国管理科学202028(2): 1-12.
  Lu W B, Huang G L, Boudt K. Stock market rise-fall forecast and quantitative investment strategy: Based on time varying MCA[J]. Chinese Journal of Management Science202028(2): 1-12.
[11] 林娟娟, 唐勇, 周小亮, 等. 北上资金、百度指数与股市关联性的时频域研究——基于协高阶矩视角[J]. 中国管理科学202230(1): 20-31.
  Lin J J, Tang Y, Zhou X L, et al. Research on the relationship between northward capital, Baidu index and stock market in time and frequency domain:Based on the perspective of higher order co-moments[J]. Chinese Journal of Management Science202230(1): 20-31.
[12] Lin C H. The comovement between exchange rates and stock prices in the Asian emerging markets[J]. International Review of Economics & Finance201222(1): 161-172.
[13] 王鹏, 蒋焰, 吴金宴.原油价格与世界股票市场之间的高阶矩相依性研究[J].管理科学201730(3): 136-146.
  Wang P, Jiang Y, Wu J Y. Dependence of higher moments between oil price and international stock markets[J]. Journal of Management Science201730(3): 136-146.
[14] 刘志峰, 张子汸, 戴鹏飞, 等. 碳市场与股票市场间的崩盘风险溢出效应研究: 新冠疫情、投资者情绪与经济政策不确定性[J]. 系统工程理论与实践202343(3): 740-754.
  Liu Z F, Zhang Z P, Dai P F, et al. A study on the spillover effect of crash risk between carbon and stock markets: COVID-19, investor sentiment and economic policy uncertainty[J]. Systems Engineering-Theory & Practice202343(3): 740-754.
[15] 崔金鑫, 邹辉文. 时频视角下国际股市间高阶矩风险溢出效应研究[J]. 国际金融研究2020(6): 75-85.
  Cui J X, Zou H W. Research on higher-moment risk spillover effects among international stock markets from the time-frequency perspective[J]. Studies of International Finance2020(6): 75-85.
[16] 周开国, 季苏楠, 杨海生. 系统性金融风险跨市场传染机制研究——基于金融协调监管视角[J]. 管理科学学报202124(7): 1-20.
  Zhou K G, Ji S N, Yang H S. Cross-market contagion mechanism of systemic risk from the perspective of coordinated supervision[J]. Journal of Management Sciences in China202124(7): 1-20.
[17] Selmi R, Mensi W, Hammoudeh S, et al. Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold[J]. Energy Economics201874: 787-801.
[18] Shahzad S J H, Bouri E, Roubaud D, et al. Is Bitcoin a better safe-haven investment than gold and commodities?[J]. International Review of Financial Analysis201963: 322-330.
[19] Jiang H, Tang S, Li L, et al. Re-examining the contagion channels of global financial crises: Evidence from the twelve years since the US subprime crisis[J]. Research in International Business and Finance202260: 101617.
[20] 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202129(7): 71-83.
  Zhao L H, Chen M Z. Systemic risk spillovers and systemic risk contributions of financial institutions in China: A perspective of dual time-varying dependence of rolling window dynamic copula model[J]. Chinese Journal of Management Science202129(7): 71-83.
[21] 熊正德, 文慧, 熊一鹏. 我国外汇市场与股票市场间波动溢出效应实证研究——基于小波多分辨的多元BEKK-GARCH(1, 1)模型分析[J]. 中国管理科学201523(4): 30-38.
  Xiong Z D, Wen H, Xiong Y P. Empirical research on spillover effect between foreign exchange market and stock market by wavelet multi-resolution analysis and multivariate BEKK-GARCH(1, 1)model[J]. Chinese Journal of Management Science201523(4): 30-38.
[22] Zhang H, Chen J, Shao L. Dynamic spillovers between energy and stock markets and their implications in the context of COVID-19[J]. International Review of Financial Analysis202177: 101828.
[23] Bargigli L, di Iasio G, Infante L, et al. The multiplex structure of interbank networks[J]. Quantitative Finance201515(4): 673-691.
[24] Krause A, Giansante S. Interbank lending and the spread of bank failures: A network model of systemic risk[J]. Journal of Economic Behavior & Organization201283(3): 583-608.
[25] 何德旭, 苗文龙, 闫娟娟, 等.全球系统性金融风险跨市场传染效应分析[J].经济研究202156(8): 4-21.
  He D X, Miao W L, Yan J J, et al. Analysis on the global systemic financial risks cross-market contagion effect[J]. Economic Research Journal202156(8): 4-21.
[26] Liu B Y, Fan Y, Ji Q, et al. High-dimensional CoVaR network connectedness for measuring conditional financial contagion and risk spillovers from oil markets to the G20 stock system[J]. Energy Economics2022105: 105749.
[27] León á, Rubio G, Serna G. Autoregresive conditional volatility, skewness and kurtosis[J]. The Quarterly Review of Economics and Finance200545(4-5): 599-618.
[28] Ando T, Greenwood-Nimmo M, Shin Y. Quantile connectedness: Modeling tail behavior in the topology of financial networks[J]. Management Science202268(4): 2401-2431.
[29] Koop G, Pesaran M H, Potter S M. Impulse response analysis in nonlinear multivariate models[J]. Journal of Econometrics199674(1): 119-147.
[30] Pesaran H H, Shin Y. Generalized impulse response analysis in linear multivariate models[J]. Economics Letters199858(1): 17-29.
[31] King M A, Wadhwani S. Transmission of volatility between stock markets[J]. The Review of Financial Studies19903(1): 5-33.
[32] Connolly R A, Wang F A. International equity market comovements: Economic fundamentals or contagion?[J].Pacific-Basin Finance Journal200311(1): 23-43.
[33] Chen J, Liang Z, Ding Q, et al. Quantile connectedness between energy, metal, and carbon markets[J]. International Review of Financial Analysis202283: 102282.
[34] 李政, 石晴, 卜林. 基于分位数关联的政策连续性跨国溢出研究[J]. 金融研究2022(8): 94-112.
  Li Z, Shi Q, Bu L. Quantile connectedness of policy continuity across the globe[J]. Journal of Financial Research2022(8): 94-112.
[35] 黄云洲, 黄炯豪, 夏晓华. 比特币价格风险、宏观经济波动与股市风险传染——基于分位数关联网络的分析[J]. 中国管理科学202432(4): 26-37.
  Huang Y Z, Huang J H, Xia X H. Bitcoin price risk, macroeconomic environment and risk contagion in China’s stock market: An analysis based on quantile coherency network[J]. Chinese Journal of Management Science202432(4): 26-37.
[36] 韩立岩, 尹力博. 投机行为还是实际需求? ——国际大宗商品价格影响因素的广义视角分析[J]. 经济研究201247(12): 83-96.
  Han L Y, Yin L B. Speculation or real demand? A multi-vision economic analysis of the international commodity prices impact factors[J]. Economic Research Journal201247(12): 83-96.
[37] 张大永, 姬强. 中国原油期货动态风险溢出研究[J]. 中国管理科学201826(11): 42-49.
  Zhang D Y, Ji Q. Studies on the dynamic risk spillovers for China’s crude oil futures[J]. Chinese Journal of Management Science201826(11): 42-49.
[38] Fruchterman T M J, Reingold E M. Graph drawing by force-directed placement[J]. Software: Practice and Experience199121(11): 1129-1164.
[39] Zhou Y, Wu S, Zhang Z. Multidimensional risk spillovers among carbon, energy and nonferrous metals markets: Evidence from the quantile VAR network[J]. Energy Economics2022114: 106319.
文章导航

/