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Dependence Structures between Sovereign Credit Default Swaps and Global Risk Factors in BRICS Countries

Author

Listed:
  • Prayer M. Rikhotso

    (School of Economics, University of Johannesburg, Johannesburg 2006, South Africa)

  • Beatrice D. Simo-Kengne

    (School of Economics, University of Johannesburg, Johannesburg 2006, South Africa)

Abstract
This study investigates the tail dependence structures of sovereign credit default swaps (CDSs) and three global risk factors in BRICS countries using a copula approach, which is popular for capturing the “true” tail dependence based on the “distribution-adjusted” joint marginals. The empirical results show that global market risk sentiment comoves with sovereign CDS spreads across BRICS countries under extreme market events such as the pandemic-induced crash of 2020, with Brazil reporting the highest bilateral convergence followed by China, Russia, and South Africa. Furthermore, oil price volatility is the second biggest risk factor correlated with CDS spreads for Brazil and South Africa, while exchange rate risk exhibits very low co-dependence with CDS spreads during extreme market downturns. On the contrary, exchange rate risk is the second largest risk factor co-moving with China and Russia’s CDS spreads, while oil price volatility exhibits the lowest co-dependence with CDS in these countries. Between oil price and currency risk, evidence of single risk factor dominance is found for Russia, where exchange rate risk is largely dominant, and policymakers could promulgate financial sector regulations that mitigate spill-over risks such as targeted capital controls when markets are distressed.

Suggested Citation

  • Prayer M. Rikhotso & Beatrice D. Simo-Kengne, 2022. "Dependence Structures between Sovereign Credit Default Swaps and Global Risk Factors in BRICS Countries," JRFM, MDPI, vol. 15(3), pages 1-22, February.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:3:p:109-:d:759360
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    References listed on IDEAS

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    1. Kalbaska, A. & Gątkowski, M., 2012. "Eurozone sovereign contagion: Evidence from the CDS market (2005–2010)," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 657-673.
    2. Spyros Spyrou & Emilios Galariotis & Panagiota Makrichoriti, 2016. "Sovereign CDS Spread Determinants and Spill-Over Effects," Proceedings of International Academic Conferences 3606062, International Institute of Social and Economic Sciences.
    3. Galariotis, Emilios C. & Makrichoriti, Panagiota & Spyrou, Spyros, 2016. "Sovereign CDS spread determinants and spill-over effects during financial crisis: A panel VAR approach," Journal of Financial Stability, Elsevier, vol. 26(C), pages 62-77.
    4. Yang, Lu & Yang, Lei & Hamori, Shigeyuki, 2018. "Determinants of dependence structures of sovereign credit default swap spreads between G7 and BRICS countries," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 19-34.
    5. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    6. Joe, Harry & Li, Haijun & Nikoloulopoulos, Aristidis K., 2010. "Tail dependence functions and vine copulas," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 252-270, January.
    7. Cyril Caillault & Dominique Guegan, 2005. "Empirical estimation of tail dependence using copulas: application to Asian markets," Quantitative Finance, Taylor & Francis Journals, vol. 5(5), pages 489-501.
    8. Choe, Geon Ho & Choi, So Eun & Jang, Hyun Jin, 2020. "Assessment of time-varying systemic risk in credit default swap indices: Simultaneity and contagiousness," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    9. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    10. Lidija Lovreta & Joaquín López Pascual, 2020. "Structural breaks in the interaction between bank and sovereign default risk," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 11(4), pages 531-559, December.
    11. Aleksey Min & Claudia Czado, 2010. "Bayesian Inference for Multivariate Copulas Using Pair-Copula Constructions," Journal of Financial Econometrics, Oxford University Press, vol. 8(4), pages 511-546, Fall.
    12. Blommestein, Hans & Eijffinger, Sylvester & Qian, Zongxin, 2016. "Regime-dependent determinants of Euro area sovereign CDS spreads," Journal of Financial Stability, Elsevier, vol. 22(C), pages 10-21.
    13. Boubaker, Heni & Sghaier, Nadia, 2013. "Portfolio optimization in the presence of dependent financial returns with long memory: A copula based approach," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 361-377.
    14. Takuya Hasebe, 2013. "Copula-based maximum-likelihood estimation of sample-selection models," Stata Journal, StataCorp LP, vol. 13(3), pages 547-573, September.
    15. Wang, Alan T. & Yang, Sheng-Yung & Yang, Nien-Tzu, 2013. "Information transmission between sovereign debt CDS and other financial factors – The case of Latin America," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 586-601.
    16. Grammatikos, Theoharry & Vermeulen, Robert, 2012. "Transmission of the financial and sovereign debt crises to the EMU: Stock prices, CDS spreads and exchange rates," Journal of International Money and Finance, Elsevier, vol. 31(3), pages 517-533.
    17. Kjersti Aas & Daniel Berg, 2009. "Models for construction of multivariate dependence - a comparison study," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 639-659.
    18. Michael Lokshin & Zurab Sajaia, 2004. "Maximum likelihood estimation of endogenous switching regression models," Stata Journal, StataCorp LP, vol. 4(3), pages 282-289, September.
    19. Iulia Cristina Iuga & Anastasia Mihalciuc, 2020. "Major Crises of the XXIst Century and Impact on Economic Growth," Sustainability, MDPI, vol. 12(22), pages 1-20, November.
    20. Tabak, Benjamin M. & de Castro Miranda, Rodrigo & da Silva Medeiros, Maurício, 2016. "Contagion in CDS, banking and equity markets," Economic Systems, Elsevier, vol. 40(1), pages 120-134.
    21. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    22. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    23. HEINEN, Andréas & VALDESOGO, Alfonso, 2009. "Asymmetric CAPM dependence for large dimensions: the Canonical Vine Autoregressive Model," LIDAM Discussion Papers CORE 2009069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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