IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i6p1396-d1096168.html
   My bibliography  Save this article

Market Volatility Spillover, Network Diffusion, and Financial Systemic Risk Management: Financial Modeling and Empirical Study

Author

Listed:
  • Sun Meng

    (School of Finance, Yunnan University of Finance and Economics, Kunming 650221, China)

  • Yan Chen

    (School of Finance, Yunnan University of Finance and Economics, Kunming 650221, China)

Abstract

With the accelerated pace of financial globalization and the gradual increase in linkages among financial markets, correctly identifying and describing the risk spillover and network diffusion in the financial system is extremely important for the prevention and management of systemic risk. Based on this, this paper takes the equity markets of 17 countries around the world from 2007 to 2022 as the research object, measures the volatility spillover effect of global financial markets using R-Vine Copula and the DY spillover index, constructs the volatility spillover network of global financial markets, discovers the spillover and diffusion pattern of global financial market risks, and provides relevant suggestions for systemic risk management. It is found that (1) there are certain aggregation characteristics in the network diffusion of global financial market volatility spillover; (2) developed European countries such as the Netherlands, France, the UK, and Germany are at the center of the network and have a strong influence; (3) Asian countries such as China, Japan, and India are at the periphery of the network; and (4) shocks from crisis events enhance the global financial market volatility spillover effect. Based on the above findings, effective prevention of global financial market risk volatility spillover and network diffusion and reduction in systemic risk need to be carried out in two ways. First, by focusing on the financial markets of key countries in the network, such as the Netherlands, the UK, France, and Germany. The second approach is to mitigate the uneven development in global financial markets and reduce the high correlation among them.

Suggested Citation

  • Sun Meng & Yan Chen, 2023. "Market Volatility Spillover, Network Diffusion, and Financial Systemic Risk Management: Financial Modeling and Empirical Study," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1396-:d:1096168
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/6/1396/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/6/1396/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhou, Wei & Chen, Yan & Chen, Jin, 2022. "Risk spread in multiple energy markets: Extreme volatility spillover network analysis before and during the COVID-19 pandemic," Energy, Elsevier, vol. 256(C).
    2. Matthias Fischer & Christian Kock & Stephan Schluter & Florian Weigert, 2009. "An empirical analysis of multivariate copula models," Quantitative Finance, Taylor & Francis Journals, vol. 9(7), pages 839-854.
    3. Farzad Alavifard, 2019. "Modelling default dependence in automotive supply networks using vine-copula," International Journal of Production Research, Taylor & Francis Journals, vol. 57(2), pages 433-451, January.
    4. Dong Hwan Oh & Andrew J. Patton, 2018. "Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 181-195, April.
    5. Tobias Michael Erhardt & Claudia Czado & Ulf Schepsmeier, 2015. "R-vine models for spatial time series with an application to daily mean temperature," Biometrics, The International Biometric Society, vol. 71(2), pages 323-332, June.
    6. Troster, Victor & Shahbaz, Muhammad & Uddin, Gazi Salah, 2018. "Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis," Energy Economics, Elsevier, vol. 70(C), pages 440-452.
    7. Mink, Mark & de Haan, Jakob, 2013. "Contagion during the Greek sovereign debt crisis," Journal of International Money and Finance, Elsevier, vol. 34(C), pages 102-113.
    8. Albulescu, Claudiu Tiberiu & Tiwari, Aviral Kumar & Ji, Qiang, 2020. "Copula-based local dependence among energy, agriculture and metal commodities markets," Energy, Elsevier, vol. 202(C).
    9. Wen, Danyan & Wang, Gang-Jin & Ma, Chaoqun & Wang, Yudong, 2019. "Risk spillovers between oil and stock markets: A VAR for VaR analysis," Energy Economics, Elsevier, vol. 80(C), pages 524-535.
    10. Aviral Kumar Tiwari & Samia Nasreen & Subhan Ullah & Muhammad Shahbaz, 2021. "Analysing spillover between returns and volatility series of oil across major stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2458-2490, April.
    11. Choi, Sun-Yong, 2022. "Dynamic volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    12. Gong, Xu & Liu, Yun & Wang, Xiong, 2021. "Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method," International Review of Financial Analysis, Elsevier, vol. 76(C).
    13. Calvo, Guillermo A. & Mendoza, Enrique G., 2000. "Rational contagion and the globalization of securities markets," Journal of International Economics, Elsevier, vol. 51(1), pages 79-113, June.
    14. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    15. Ulf Schepsmeier, 2019. "A goodness-of-fit test for regular vine copula models," Econometric Reviews, Taylor & Francis Journals, vol. 38(1), pages 25-46, January.
    16. Algieri, Bernardina & Leccadito, Arturo, 2017. "Assessing contagion risk from energy and non-energy commodity markets," Energy Economics, Elsevier, vol. 62(C), pages 312-322.
    17. Zhang, Yongjie & Wang, Meng & Xiong, Xiong & Zou, Gaofeng, 2021. "Volatility spillovers between stock, bond, oil, and gold with portfolio implications: Evidence from China," Finance Research Letters, Elsevier, vol. 40(C).
    18. Jian Yang & Yinggang Zhou, 2013. "Credit Risk Spillovers Among Financial Institutions Around the Global Credit Crisis: Firm-Level Evidence," Management Science, INFORMS, vol. 59(10), pages 2343-2359, October.
    19. Charlotte Christiansen, 2007. "Volatility‐Spillover Effects in European Bond Markets," European Financial Management, European Financial Management Association, vol. 13(5), pages 923-948, November.
    20. Ghulam Ali, 2013. "EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 2(3), pages 1-6.
    21. Sang Jin Lee, 2009. "Volatility spillover effects amongsix Asian countries," Applied Economics Letters, Taylor & Francis Journals, vol. 16(5), pages 501-508.
    22. Khalil Jebran & Amjad Iqbal, 2016. "Dynamics of volatility spillover between stock market and foreign exchange market: evidence from Asian Countries," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-20, December.
    23. Zhang, Xiaoming & Zhang, Tong & Lee, Chien-Chiang, 2022. "The path of financial risk spillover in the stock market based on the R-vine-Copula model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    24. Zhou, Xiangyi & Zhang, Weijin & Zhang, Jie, 2012. "Volatility spillovers between the Chinese and world equity markets," Pacific-Basin Finance Journal, Elsevier, vol. 20(2), pages 247-270.
    25. Yu, Lean & Zha, Rui & Stafylas, Dimitrios & He, Kaijian & Liu, Jia, 2020. "Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models," International Review of Financial Analysis, Elsevier, vol. 68(C).
    26. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & Perez de Gracia, Fernando, 2018. "Oil volatility, oil and gas firms and portfolio diversification," Energy Economics, Elsevier, vol. 70(C), pages 499-515.
    27. Zhou, Wei & Gu, Qinen & Chen, Jin, 2021. "From volatility spillover to risk spread: An empirical study focuses on renewable energy markets," Renewable Energy, Elsevier, vol. 180(C), pages 329-342.
    28. Ardia, David & Bluteau, Keven & Rüede, Maxime, 2019. "Regime changes in Bitcoin GARCH volatility dynamics," Finance Research Letters, Elsevier, vol. 29(C), pages 266-271.
    29. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    30. Pho, Kim Hung & Ly, Sel & Lu, Richard & Hoang, Thi Hong Van & Wong, Wing-Keung, 2021. "Is Bitcoin a better portfolio diversifier than gold? A copula and sectoral analysis for China," International Review of Financial Analysis, Elsevier, vol. 74(C).
    31. Ma, Yiqun & Wang, Junhao, 2019. "Co-movement between oil, gas, coal, and iron ore prices, the Australian dollar, and the Chinese RMB exchange rates: A copula approach," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    32. Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
    33. Claudiu Albulescu & Aviral Tiwari & Qiang Ji, 2020. "Copula-based local dependence between energy, agriculture and metal commodity markets," Papers 2003.04007, arXiv.org.
    34. Dalu Zhang & Meilan Yan & Andreas Tsopanakis, 2018. "Financial stress relationships among Euro area countries: an R-vine copula approach," The European Journal of Finance, Taylor & Francis Journals, vol. 24(17), pages 1587-1608, November.
    35. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    36. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    37. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
    38. Mukul Bhatnagar & Ercan Özen & Sanjay Taneja & Simon Grima & Ramona Rupeika-Apoga, 2022. "The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach," Risks, MDPI, vol. 10(11), pages 1-16, November.
    39. Nekhili, Ramzi & Bouri, Elie, 2023. "Higher-order moments and co-moments' contribution to spillover analysis and portfolio risk management," Energy Economics, Elsevier, vol. 119(C).
    40. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    41. Katsiampa, Paraskevi & Corbet, Shaen & Lucey, Brian, 2019. "Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis," Finance Research Letters, Elsevier, vol. 29(C), pages 68-74.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Asadi, Mehrad & Roubaud, David & Tiwari, Aviral Kumar, 2022. "Volatility spillovers amid crude oil, natural gas, coal, stock, and currency markets in the US and China based on time and frequency domain connectedness," Energy Economics, Elsevier, vol. 109(C).
    2. Zhang, Hua & Chen, Jinyu & Shao, Liuguo, 2021. "Dynamic spillovers between energy and stock markets and their implications in the context of COVID-19," International Review of Financial Analysis, Elsevier, vol. 77(C).
    3. Khalfaoui, Rabeh & Baumöhl, Eduard & Sarwar, Suleman & Výrost, Tomáš, 2021. "Connectedness between energy and nonenergy commodity markets: Evidence from quantile coherency networks," Resources Policy, Elsevier, vol. 74(C).
    4. Elsayed, Ahmed H. & Nasreen, Samia & Tiwari, Aviral Kumar, 2020. "Time-varying co-movements between energy market and global financial markets: Implication for portfolio diversification and hedging strategies," Energy Economics, Elsevier, vol. 90(C).
    5. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Adewuyi, Adeolu O. & Lee, Chien-Chiang, 2022. "Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Energy Economics, Elsevier, vol. 113(C).
    6. Zhou, Wei & Chen, Yan & Chen, Jin, 2022. "Risk spread in multiple energy markets: Extreme volatility spillover network analysis before and during the COVID-19 pandemic," Energy, Elsevier, vol. 256(C).
    7. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
    8. Xu Zhang & Xian Yang & Jianping Li & Jun Hao, 2023. "Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 705-733, June.
    9. Gong, Xiao-Li & Zhao, Min & Wu, Zhuo-Cheng & Jia, Kai-Wen & Xiong, Xiong, 2023. "Research on tail risk contagion in international energy markets—The quantile time-frequency volatility spillover perspective," Energy Economics, Elsevier, vol. 121(C).
    10. Chen, Peng & He, Limin & Yang, Xuan, 2021. "On interdependence structure of China's commodity market," Resources Policy, Elsevier, vol. 74(C).
    11. Mensi, Walid & Al Rababa'a, Abdel Razzaq & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets," Energy Economics, Elsevier, vol. 98(C).
    12. Dimitrios Louzis, 2015. "Measuring spillover effects in Euro area financial markets: a disaggregate approach," Empirical Economics, Springer, vol. 49(4), pages 1367-1400, December.
    13. Sowmya, Subramaniam & Prasanna, Krishna & Bhaduri, Saumitra, 2016. "Linkages in the term structure of interest rates across sovereign bond markets," Emerging Markets Review, Elsevier, vol. 27(C), pages 118-139.
    14. Awartani, Basel & Maghyereh, Aktham I. & Shiab, Mohammad Al, 2013. "Directional spillovers from the U.S. and the Saudi market to equities in the Gulf Cooperation Council countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 27(C), pages 224-242.
    15. Gaete, Michael & Herrera, Rodrigo, 2023. "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach," Journal of Commodity Markets, Elsevier, vol. 32(C).
    16. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2021. "Volatility spillovers during market supply shocks: The case of negative oil prices," Resources Policy, Elsevier, vol. 74(C).
    17. Amar, Amine Ben & Goutte, Stéphane & Isleimeyyeh, Mohammad & Benkraiem, Ramzi, 2022. "Commodity markets dynamics: What do cross-commodities over different nearest-to-maturities tell us?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    18. Khalfaoui, Rabeh & Shahzad, Umer & Ghaemi Asl, Mahdi & Ben Jabeur, Sami, 2023. "Investigating the spillovers between energy, food, and agricultural commodity markets: New insights from the quantile coherency approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 63-80.
    19. Cui, Jinxin & Goh, Mark & Li, Binlin & Zou, Huiwen, 2021. "Dynamic dependence and risk connectedness among oil and stock markets: New evidence from time-frequency domain perspectives," Energy, Elsevier, vol. 216(C).
    20. Zhao, Yihang & Zhou, Zhenxi & Zhang, Kaiwen & Huo, Yaotong & Sun, Dong & Zhao, Huiru & Sun, Jingqi & Guo, Sen, 2023. "Research on spillover effect between carbon market and electricity market: Evidence from Northern Europe," Energy, Elsevier, vol. 263(PF).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1396-:d:1096168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.