Risk spillover between financial markets based on non-parametric quantile Vine-Copula model

Mengwei Wang; Junxiang Lu; Yu Zheng; Weiqin Zhu; Chunyu Xie

doi:10.47297/wspdecWSP2515-797318.20250901

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Risk spillover between financial markets based on non-parametric quantile Vine-Copula model

Volume 9, Issue 1

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Risk spillover between financial markets based on non-parametric quantile Vine-Copula model
Risk spillover between financial markets based on non-parametric quantile Vine-Copula model
1.Xi'an Polytechnic University, Xi'an, 710600, China
2.Hebei Vocational University of Industry and Technology, Shijiazhuang, 050091, China
Vol. 9, Issue 1, Pages: 90-94(2025)
Published：21 January 2025
DOI：10.47297/wspdecWSP2515-797318.20250901
Accepted：

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Wang Mengwei,Lu Junxiang,Zheng Yu,et al.Risk spillover between financial markets based on non-parametric quantile Vine-Copula model[J].Development Economics of China,2025,09(01):90-94.
DOI：

Wang Mengwei,Lu Junxiang,Zheng Yu,et al.Risk spillover between financial markets based on non-parametric quantile Vine-Copula model[J].Development Economics of China,2025,09(01):90-94. DOI： 10.47297/wspdecWSP2515-797318.20250901.

摘要

Abstract

Brics (China

Brazil

Russia

India

South Africa) as the world's emerging economies

the stability of their financial markets has an important impact on the global economy. This paper intends to study the correlation structure and risk contagion effect among the financial markets of the BRICS countries. Based on the stock market

an important indicator of the financial markets of each country

the paper uses the non-parametric quantile model to fit the marginal distribution of the returns of each country

constructs the D-Vine Copula model

explores the correlation structure among the financial markets of each country

and uses CoES to describe the risk spillover intensity. The empirical results show that China's financial market has significant risk spillover effect on other BRICS countries

and the impact on Russia is the most prominent

followed by India and South Africa

and the impact on Brazil is relatively weak and presents obvious asymmetry. These results have important theoretical value and practical significance for monitoring the risk contagion effect in BRICS countries.

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references

Cheng F H , Gutierrez M , Mahajan A . A future global economy to be built by BRICs [J ] . Global Finance Journal , 2006 , 18 ( 2 ): 143 - 156 .

Chen Yangyang . Study on net contagion Mechanism of extreme risk in BRICS Financial Markets [D ] . Guangdong University of Finance and Economics , 2014 .

Luo Jia . The impact of global exchange consolidation on BRICS countries and its countermeasures [J ] . Financial Education Research , 2017 , 30 ( 2 ): 66 - 73 .

Sklar A . Fonctions de repartition an dimensions et leursmarges [J ] . Publication deinsititut de Statistiquede University de Paris , 1959 , 8 : 229 - 231 .

Yang , Hamori . Dependence structure among international stock markets: a GARCH-copula analysis [J ] . Applied Financial Economics , 2013 , 23 : 1805 - 1817 .

Fan J , Li R . Variable selection via nonconcave penalized likelihood and its oracle properties [J ] . Journal of the American statistical Association , 2001 , 96 ( 456 ): 1348 - 1360 .

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

Brechmann C , Czado , C , Aas K . Pair-copula constructions for high-dimensional multivariate time series of counts . Computational Statistics & Data Analysis , 2012 , 56(⅛): 3877- 3900 .

Xu Jiaqing , Lu Junxiang . Risk analysis among BRICS financial markets under the COVID-19 epidemic based on Vine-Copula model [J ] . Journal of Ningxia University (Natural Science Edition) , 2022 , 43 ( 3 ): 239 - 253 .

Kraus D , Czado C . D-vine copula based quantile regression [J ] . Computational Statistics & Data Analysis , 2017 , 110 : 1 - 18 .

Ye Wuyi , GUO Renzhen , MIAO Baiqi . Financial contagiosity and stability test of BRIC countries based on Rattan copula change point Model [J ] . Journal of University of Science and Technology of China , 2018 , 48 ( 8 ): 655 - 666 .

Adrian T , Brunnermeier M K . CoVaR [R ] . National Bureau of Economic Research , 2011 .

Girardi G , Ergün A T . Systemic risk measurement: Multivariate GARCH estimation of CoVaR [J ] . Journal of Banking & Finance , 2013 , 37 ( 8 ): 3169 - 3180 .

Zhang Bingjie , Wang Shouyang , WEI Yunjie . Measurement of financial systemic risk based on CoES model [J ] . Systems Engineering Theory and Practice , 2018 , 38 ( 3 ): 565 - 575 .

Cao Jie , Lei Lianghai . Research on Stock Market risk spillover based on HAC-Generalized Multidimensional CoES model [J ] . Statistical Research , 2022 , 39 ( 3 ): 142 - 153 .

Parzen E . On estimation of a probability density function and mode [J ] . The annals of mathematical statistics , 1962 , 33 ( 3 ): 1065 - 1076 .

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 : 245 - 268 .

Ren X , Tian Y , Li S J . Vine copula-based dependence description for multivariate multimode process monitoring [J ] . Industrial & Engineering Chemistry Research , 2015 , 54 ( 41 ): 10001 - 10019 .

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