Financial contagion and stability test of BRIC countries based on R-vine copula change-point model

YE Wuyi; GUO Renzhen; MIAO Baiqi

doi:10.3969/j.issn.0253-2778.2018.08.009

JUSTC > 2018 > 48(8): 655-666. > DOI: 10.3969/j.issn.0253-2778.2018.08.009

PDF (1140 KB)

Open Access JUSTC Original Paper

Financial contagion and stability test of BRIC countries based on R-vine copula change-point model

Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China

Cite this: JUSTC, 2018, 48(8): 655-666

https://doi.org/10.3969/j.issn.0253-2778.2018.08.009

More Information

Received Date: December 12, 2017
Revised Date: February 24, 2018
Accepted Date: February 24, 2018
Published Date: August 30, 2018

Full text PDF

Abstract

Abstract

The BRIC countries (Brazil, Russia, India and China) are representations of the emerging markets. Financial contagion and stability from the perspective of the dependence of the analyzed countries on the global systemic risk was characterized, with the MSCI Global Index representing the global systemic risk factor. The dependency structure of the BRIC’s major indexes and the MSCI global index was analyzed, and an empirical analysis of the financial stability of the BRIC countries was performed. To analyze the structural changes of systemic risk impact on the BRIC countries, R-vine copula was used to test the change point and analyze the financial contagion and stability of the BRIC countries affected by the financial crisis and BRICs events. The financial stability among BRIC countries was measured using the correlation coefficient based on the MSCI index. The empirical results show that after the control of systemic risk, the independence of the BRIC countries’ stock markets has been strengthened. The BRIC countries have been hit harder by systemic risks after the financial crisis. Strengthening BRIC countries’ cooperation will help to reduce the risk of the affection between BRIC countries.

Abstract

The BRIC countries (Brazil, Russia, India and China) are representations of the emerging markets. Financial contagion and stability from the perspective of the dependence of the analyzed countries on the global systemic risk was characterized, with the MSCI Global Index representing the global systemic risk factor. The dependency structure of the BRIC’s major indexes and the MSCI global index was analyzed, and an empirical analysis of the financial stability of the BRIC countries was performed. To analyze the structural changes of systemic risk impact on the BRIC countries, R-vine copula was used to test the change point and analyze the financial contagion and stability of the BRIC countries affected by the financial crisis and BRICs events. The financial stability among BRIC countries was measured using the correlation coefficient based on the MSCI index. The empirical results show that after the control of systemic risk, the independence of the BRIC countries’ stock markets has been strengthened. The BRIC countries have been hit harder by systemic risks after the financial crisis. Strengthening BRIC countries’ cooperation will help to reduce the risk of the affection between BRIC countries.

FullText(HTML)

References (35)

References

[1]	FISHER I. The debt-deflation theory of great depressions[J].Econometrica: Journal of the Econometric Society, 1933: 337-357.
[2]	KEYNES J M. The General Theory of Employment, Interest and Money[M]. New York:Harcout, Brace and World, 1936.
[3]	FRANKEL J A, ROSE A K. Currency crashes in emerging markets: An empirical treatment[J]. Journal of International Economics, 1996, 41(3): 351-366.
[4]	BUSSIERE M, FRATZSCHER M. Towards a new early warning system of financial crises [J]. Journal of International Money and Finance, 2006, 25(6): 953-973.
[5]	ALBULESCU C T. Forecasting the Romanian financial system stability using a stochastic simulation model [J]. Romanian Journal of Economic Forecasting, 2010, 13(1): 81-98.
[6]	ALBULESCU C T. Financial stability and monetary policy: A reduced-form model for the euro area [J]. Romanian Journal of Economic Forecasting, 2013, 16(1): 62-81.
[7]	STIGLITZ J. Financial market stability and monetary policy [J]. Pacific Economic Review, 2002, 7(1): 13-30.
[8]	BERNOTH K, PICK A. Forecasting the fragility of the banking and insurance sectors[J]. Journal of Banking & Finance, 2011,35(4): 807-818.
[9]	BUNN P, REDWOOD V. Company accounts based modelling of business failures and the implications for financial stability[R]. London: Bank of England, 2003.
[10]	UHDE A, HEIMESHOFF U. Consolidation in banking and financial stability in Europe: Empirical evidence[J]. Journal of Banking & Finance, 2009, 33(7): 1299-1311.
[11]	ELSINGER H, LEHAR A, SUMMER M. Risk assessment for banking systems[J]. Management Science, 2006, 52(9): 1301-1314.
[12]	SENSOY A, OZTURK K, HACIHASANOGLU E. Constructing a financial fragility index for emerging countries[J]. Finance Research Letters, 2014, 11(4): 410-419.
[13]	马亚明, 赵慧. 热钱流动对资产价格波动和金融脆弱性的影响——基于 SVAR 模型的实证分析[J]. 现代财经: 天津财经学院学报, 2012 (6): 5-15. MA Yaming, ZHAO Hui. On the effect of hot money flows on asset price fluctuation and financial fragility: Based on empirical analysis of SVAR model[J]. Modern Finance and Economics: Journal of Tianjin University of Finance and Economics，2012(6):5-15.
[14]	文凤华, 张阿兰, 戴志锋, 等. 房地产价格波动与金融脆弱性——基于中国的实证研究[J]. 中国管理科学, 2012, 20(2): 1-10. WEN Fenghua, ZHANG Alan, DAI Zhifeng, et al. Price fluctuation of real estate industry and financial fragility: An empirical study based on Chinese [J]. Chinese Journal of Management Science, 2012, 20(2): 1-10.
[15]	郭红兵, 杜金岷. 中国金融稳定状况指数的构建[J]. 数量经济技术经济研究, 2014, 31(5): 100-116. GUO Hongbing, DU Jinmin. Construction of China’s financial stability conditions index[J]. The Journal of Quantitative & Technical Economics, 2014, 31(5): 100-116.
[16]	邓创, 王思怡, 甘喆. 中国金融稳定性的度量及其与主要宏观经济变量的关系[J]. 数量经济研究, 2016, 7(1): 51-69. DENG Chuang, WANG Siyi, GAN Zhe. The financial soundness and its relationship with main macroeconomic variables in China[J]. The Journal of Quantitative Economics, 2016, 7(1): 51-69.
[17]	杨光, 李力, 郝大鹏. 零利率下限, 货币政策与金融稳定[J]. 财经研究, 2017, 43(1): 41-50. YANG Guang, LI Li, HAO Dapeng. The zero lower bound, monetary policy and financial stability[J]. Journal of Finance and Economics, 2017, 43(1): 41-50.
[18]	叶五一, 缪柏其, 吴振翔. 基于Copula方法的条件VaR估计[J]. 中国科学技术大学学报, 2006, 36(9): 917-922. YE Wuyi, MIAO Baiqi, WU Zhenxiang. Estimating conditional VaR based on Copula method[J]. Journal of University of Science and Technology of China, 2006, 36(9): 917-922.
[19]	王璐, 黄登仕, 魏宇. 国际多元化下投资组合优化研究:动态Copula方法 [J]. 数理统计与管理, 2016, 35(6): 1109-1124. WANG Lu, HUANG Dengshi, WEI Yu. Research on portfolio optimization under international diversification: dynamic Copula[J]. Journal of Applied Statistics and Management, 2016, 35(6): 1109-1124.
[20]	李平, 尹菁华, 来娜,等. 基于Copula双变量模拟的CoCo债券定价[J]. 系统工程学报, 2016, 31(6): 772-782. LI Ping, YIN Jinghua, LAI Na, et al. CoCo bonds pricing based on Copulas bivariate simulation[J]. Journal of Systems Engineering, 2016, 31(6): 772-782.
[21]	黄友珀, 唐振鹏, 唐勇. 基于藤copula-已实现GARCH的组合收益分位数预测[J]. 系统工程学报, 2016, 31(1): 45-54. HUANG Youpo, TANG Zhenpeng, TANG Yong. Portfolio quantile forecasts based on vine copula and realized GARCH[J]. Journal of Systems Engineering, 2016, 31(1): 45-54.
[22]	韩超, 严太华. 基于高维动态藤Copula的汇率组合风险分析[J]. 中国管理科学, 2017, 25(2): 10-20. HAN Chao, YAN Taihua. Risk analysis of foreign exchange portfolios based on high-dimensional dynamic vine copula[J]. Chinese Journal of Management Science, 2017, 25(2): 10-20.
[23]	Costa Dias A. Copula inference for finance and insurance[D]. Zurich, Switzerland: ETH Zurich, 2004.
[24]	韦艳华, 张世英. 金融市场动态相关结构的研究[J]. 系統工程学报, 2006, 21(3): 313-317. WEI Yanhua, ZHANG Shiying. Research on dynamic dependence structure of financial markets[J]. Journal of Systems Engineering, 2006, 21(3): 313-317.
[25]	BEDFORD T, COOKE R M. Vines: A new graphical model for dependent random variables [J]. Annals of Statistics, 2002, 30(4): 1031-1068.
[26]	AAS K, CZADO C, FRIGESSI A, et al. Pair-copula constructions of multiple dependence [J]. Insurance: Mathematics and Economics, 2009, 44(2): 182-198.
[27]	MORALES NPOLES O, COOKE R M, KUROWICKA D. About the number of vines and regular vines on n nodes[R]. Delft, Netherlands: Delft University of Technology, 2010.
[28]	BRECHMANN E C, CZADO C. Risk management with high-dimensional vine copulas: An analysis of the EuroStoxx 50[J]. Statistics & Risk Modeling, 2013, 30(4): 307-342.
[29]	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]. 系统工程理论与实践, 2015, 35(1): 26-36. MA Feng, WEI Yu, HUANG Dengshi. Measurement of dynamic stock portfolio VaR and its forecasting model based on vine copula[J]. Systems Engineering: Theory & Practice, 2015, 35(1): 26-36.
[30]	申敏. 基于正则藤Copula的行业系统性信用风险传染分析[J]. 工业技术经济, 2016, 35(6): 52-61. SHEN Min. Analysis of systemic credit risk contagion among industries based on R-vine Copula model[J]. Journal of Industrial Technological & Economics, 2016, 35(6): 52-61.
[31]	DISSMANN J, BRECHMANN E C, CZADO C, et al. Selecting and estimating regular vine copulae and application to financial returns [J]. Computational Statistics & Data Analysis, 2013, 59: 52-69.
[32]	MORALES NPOLES O. Bayesian belief nets and vines in aviation safety and other applications [D]. Delft, Nederland: Delft University of Technology, 2010.
[33]	DISSMANN J. Statistical inference for regular vines and application [D].München,Germany: Technische Universitat München, 2010.
[34]	CSRG M, HORVTH L. Limit Theorems in Change-Point Analysis [M]. New York: Wiley, 1997.
[35]	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.)

Cited By

Track Citations

Get Citation

{{if article.articleBusiness.pdfLink && article.articleBusiness.pdfLink != ''}} {{else}} {{/if}}PDF

XML

References

[1]	FISHER I. The debt-deflation theory of great depressions[J].Econometrica: Journal of the Econometric Society, 1933: 337-357.
[2]	KEYNES J M. The General Theory of Employment, Interest and Money[M]. New York:Harcout, Brace and World, 1936.
[3]	FRANKEL J A, ROSE A K. Currency crashes in emerging markets: An empirical treatment[J]. Journal of International Economics, 1996, 41(3): 351-366.
[4]	BUSSIERE M, FRATZSCHER M. Towards a new early warning system of financial crises [J]. Journal of International Money and Finance, 2006, 25(6): 953-973.
[5]	ALBULESCU C T. Forecasting the Romanian financial system stability using a stochastic simulation model [J]. Romanian Journal of Economic Forecasting, 2010, 13(1): 81-98.
[6]	ALBULESCU C T. Financial stability and monetary policy: A reduced-form model for the euro area [J]. Romanian Journal of Economic Forecasting, 2013, 16(1): 62-81.
[7]	STIGLITZ J. Financial market stability and monetary policy [J]. Pacific Economic Review, 2002, 7(1): 13-30.
[8]	BERNOTH K, PICK A. Forecasting the fragility of the banking and insurance sectors[J]. Journal of Banking & Finance, 2011,35(4): 807-818.
[9]	BUNN P, REDWOOD V. Company accounts based modelling of business failures and the implications for financial stability[R]. London: Bank of England, 2003.
[10]	UHDE A, HEIMESHOFF U. Consolidation in banking and financial stability in Europe: Empirical evidence[J]. Journal of Banking & Finance, 2009, 33(7): 1299-1311.
[11]	ELSINGER H, LEHAR A, SUMMER M. Risk assessment for banking systems[J]. Management Science, 2006, 52(9): 1301-1314.
[12]	SENSOY A, OZTURK K, HACIHASANOGLU E. Constructing a financial fragility index for emerging countries[J]. Finance Research Letters, 2014, 11(4): 410-419.
[13]	马亚明, 赵慧. 热钱流动对资产价格波动和金融脆弱性的影响——基于 SVAR 模型的实证分析[J]. 现代财经: 天津财经学院学报, 2012 (6): 5-15. MA Yaming, ZHAO Hui. On the effect of hot money flows on asset price fluctuation and financial fragility: Based on empirical analysis of SVAR model[J]. Modern Finance and Economics: Journal of Tianjin University of Finance and Economics，2012(6):5-15.
[14]	文凤华, 张阿兰, 戴志锋, 等. 房地产价格波动与金融脆弱性——基于中国的实证研究[J]. 中国管理科学, 2012, 20(2): 1-10. WEN Fenghua, ZHANG Alan, DAI Zhifeng, et al. Price fluctuation of real estate industry and financial fragility: An empirical study based on Chinese [J]. Chinese Journal of Management Science, 2012, 20(2): 1-10.
[15]	郭红兵, 杜金岷. 中国金融稳定状况指数的构建[J]. 数量经济技术经济研究, 2014, 31(5): 100-116. GUO Hongbing, DU Jinmin. Construction of China’s financial stability conditions index[J]. The Journal of Quantitative & Technical Economics, 2014, 31(5): 100-116.
[16]	邓创, 王思怡, 甘喆. 中国金融稳定性的度量及其与主要宏观经济变量的关系[J]. 数量经济研究, 2016, 7(1): 51-69. DENG Chuang, WANG Siyi, GAN Zhe. The financial soundness and its relationship with main macroeconomic variables in China[J]. The Journal of Quantitative Economics, 2016, 7(1): 51-69.
[17]	杨光, 李力, 郝大鹏. 零利率下限, 货币政策与金融稳定[J]. 财经研究, 2017, 43(1): 41-50. YANG Guang, LI Li, HAO Dapeng. The zero lower bound, monetary policy and financial stability[J]. Journal of Finance and Economics, 2017, 43(1): 41-50.
[18]	叶五一, 缪柏其, 吴振翔. 基于Copula方法的条件VaR估计[J]. 中国科学技术大学学报, 2006, 36(9): 917-922. YE Wuyi, MIAO Baiqi, WU Zhenxiang. Estimating conditional VaR based on Copula method[J]. Journal of University of Science and Technology of China, 2006, 36(9): 917-922.
[19]	王璐, 黄登仕, 魏宇. 国际多元化下投资组合优化研究:动态Copula方法 [J]. 数理统计与管理, 2016, 35(6): 1109-1124. WANG Lu, HUANG Dengshi, WEI Yu. Research on portfolio optimization under international diversification: dynamic Copula[J]. Journal of Applied Statistics and Management, 2016, 35(6): 1109-1124.
[20]	李平, 尹菁华, 来娜,等. 基于Copula双变量模拟的CoCo债券定价[J]. 系统工程学报, 2016, 31(6): 772-782. LI Ping, YIN Jinghua, LAI Na, et al. CoCo bonds pricing based on Copulas bivariate simulation[J]. Journal of Systems Engineering, 2016, 31(6): 772-782.
[21]	黄友珀, 唐振鹏, 唐勇. 基于藤copula-已实现GARCH的组合收益分位数预测[J]. 系统工程学报, 2016, 31(1): 45-54. HUANG Youpo, TANG Zhenpeng, TANG Yong. Portfolio quantile forecasts based on vine copula and realized GARCH[J]. Journal of Systems Engineering, 2016, 31(1): 45-54.
[22]	韩超, 严太华. 基于高维动态藤Copula的汇率组合风险分析[J]. 中国管理科学, 2017, 25(2): 10-20. HAN Chao, YAN Taihua. Risk analysis of foreign exchange portfolios based on high-dimensional dynamic vine copula[J]. Chinese Journal of Management Science, 2017, 25(2): 10-20.
[23]	Costa Dias A. Copula inference for finance and insurance[D]. Zurich, Switzerland: ETH Zurich, 2004.
[24]	韦艳华, 张世英. 金融市场动态相关结构的研究[J]. 系統工程学报, 2006, 21(3): 313-317. WEI Yanhua, ZHANG Shiying. Research on dynamic dependence structure of financial markets[J]. Journal of Systems Engineering, 2006, 21(3): 313-317.
[25]	BEDFORD T, COOKE R M. Vines: A new graphical model for dependent random variables [J]. Annals of Statistics, 2002, 30(4): 1031-1068.
[26]	AAS K, CZADO C, FRIGESSI A, et al. Pair-copula constructions of multiple dependence [J]. Insurance: Mathematics and Economics, 2009, 44(2): 182-198.
[27]	MORALES NPOLES O, COOKE R M, KUROWICKA D. About the number of vines and regular vines on n nodes[R]. Delft, Netherlands: Delft University of Technology, 2010.
[28]	BRECHMANN E C, CZADO C. Risk management with high-dimensional vine copulas: An analysis of the EuroStoxx 50[J]. Statistics & Risk Modeling, 2013, 30(4): 307-342.
[29]	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]. 系统工程理论与实践, 2015, 35(1): 26-36. MA Feng, WEI Yu, HUANG Dengshi. Measurement of dynamic stock portfolio VaR and its forecasting model based on vine copula[J]. Systems Engineering: Theory & Practice, 2015, 35(1): 26-36.
[30]	申敏. 基于正则藤Copula的行业系统性信用风险传染分析[J]. 工业技术经济, 2016, 35(6): 52-61. SHEN Min. Analysis of systemic credit risk contagion among industries based on R-vine Copula model[J]. Journal of Industrial Technological & Economics, 2016, 35(6): 52-61.
[31]	DISSMANN J, BRECHMANN E C, CZADO C, et al. Selecting and estimating regular vine copulae and application to financial returns [J]. Computational Statistics & Data Analysis, 2013, 59: 52-69.
[32]	MORALES NPOLES O. Bayesian belief nets and vines in aviation safety and other applications [D]. Delft, Nederland: Delft University of Technology, 2010.
[33]	DISSMANN J. Statistical inference for regular vines and application [D].München,Germany: Technische Universitat München, 2010.
[34]	CSRG M, HORVTH L. Limit Theorems in Change-Point Analysis [M]. New York: Wiley, 1997.
[35]	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.)

[1]	TANG Heng, ZHENG Zhi, ZHANG Weiping. A robust homogeneity pursuit algorithm for varying coefficient models with longitudinal data[J]. JUSTC, 2021, 51(12): 857-867. DOI: 10.52396/JUST-2021-0054
[2]	YUAN Zhenfei, HU Taizhong. Sampling multivariate count variables with prespecified Pearson correlation using marginal regular vine copulas[J]. JUSTC, 2020, 50(10): 1291. DOI: 10.3969/j.issn.0253-2778.2020.10.002
[3]	TAN Changchun, JIANG Min. Influence analysis of tuning parameters on the change-point estimation in CUSUM type statistics[J]. JUSTC, 2020, 50(7): 920-928. DOI: 10.3969/j.issn.0253-2778.2020.07.008
[4]	LIU Jie, CHEN Xiaoyuan, WU Zun. Change-points estimation and model selection for piecewise stationary autoregressive processes based on modified adaptive LASSO method[J]. JUSTC, 2020, 50(6): 744-751. DOI: 10.3969/j.issn.0253-2778.2020.06.005
[5]	YE Wuyi, DING Yalin, JIAO Shoukun. Dynamic dependence of return and volatility between BRICS stock markets based on TV-Copula-X model[J]. JUSTC, 2020, 50(5): 612-628. DOI: 10.3969/j.issn.0253-2778.2020.05.009
[6]	FANG Weijie, JIN Baisuo. Limit properties of weighted cumulative sum estimator of change-point in variance[J]. JUSTC, 2020, 50(4): 389-395. DOI: 10.3969/j.issn.0253-2778.2020.04.001
[7]	LI Yingtian, HU Haiming (for the BESIII Collaboration). Status of R Scan at BESIII[J]. JUSTC, 2016, 46(4): 292-300. DOI: 10.3969/j.issn.0253-2778.2016.04.006
[8]	DU Binbin, LING Qiang, LI Feng, SUN Tao. Grid-like radar detection based on the distribution of key points[J]. JUSTC, 2015, 45(10): 864-870. DOI: 10.3969/j.issn.0253-2778.2015.10.009
[9]	TAN Changchun, YU Xiangxiang, DONG Cuiling. Convergence rate of location parameter change-point estimator[J]. JUSTC, 2015, 45(3): 210-219. DOI: 10.3969/j.issn.0253-2778.2015.03.006
[10]	SONG Xianmei, ZHU Shixin. Constacyclic codes over Rk[J]. JUSTC, 2014, 44(3): 203-206. DOI: 10.3969/j.issn.0253-2778.2014.03.006

TrendMD

Volume 48 Issue 8 PP. 655-666

Cover

Keywords

Article Metrics

Article views (107) PDF downloads (579)

Financial contagion and stability test of BRIC countries based on R-vine copula change-point model

Abstract

Abstract

References

Related Articles

Catalog

References

Related Articles

TrendMD

Article Metrics

Authors

Browse

Contact Us

About

Financial contagion and stability test of BRIC countries based on R-vine copula change-point model

Share

Tools

Abstract

Abstract

References

Related Articles

Catalog

References

Related Articles

TrendMD

Article Metrics

Authors

Browse

Contact Us

About

Export File

Citation

Format

Content