IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v59y2020i4d10.1007_s00181-019-01695-4.html
   My bibliography  Save this article

GAS Copula models on who’s systemically important in South Africa: Banks or Insurers?

Author

Listed:
  • Mathias Mandla Manguzvane

    (University of Johannesburg)

  • John Weirstrass Muteba Mwamba

    (University of Johannesburg)

Abstract

This paper makes use of the generalized autoregressive score (GAS) Copula model to estimate the Conditional Value at Risk (CoVaR) measure of systemic risk. The proposed measure of systemic risk considers the score of the conditional density as the main driver of time-varying dynamics of tail dependence among financial institutions. Not only does the GAS Copula-based CoVaR enable us to monitor the amount of systemic risk posed by different financial institutions at a specific date, it also allows for the forecasting of systemic risk over time. Our results based on a sample of daily equity returns collected from January 2000 to July 2017 surprisingly show that in South Africa, insurers are the most systemically risky compared to banks and other financial sectors. Moreover, we make use of flexible GAS Copulas in order to approximate complex dependence structures. To validate the robustness of our results over time, we divide our sample period into two sub samples, namely the pre-crisis period (January 2000 to June 2007) and the post-crisis period (January 2010 to July 2017). We obtain similar results in the pre-crisis period. However, in the post-crisis period banks are found to be the biggest threat to system-wide stability.

Suggested Citation

  • Mathias Mandla Manguzvane & John Weirstrass Muteba Mwamba, 2020. "GAS Copula models on who’s systemically important in South Africa: Banks or Insurers?," Empirical Economics, Springer, vol. 59(4), pages 1573-1604, October.
    • Handle: RePEc:spr:empeco:v:59:y:2020:i:4:d:10.1007_s00181-019-01695-4
    DOI: 10.1007/s00181-019-01695-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-019-01695-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00181-019-01695-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Huang, Xin & Zhou, Hao & Zhu, Haibin, 2009. "A framework for assessing the systemic risk of major financial institutions," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2036-2049, November.
    3. Koopman, Siem Jan & Lucas, André, 2008. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 510-525.
    4. Guido Lorenzoni, 2008. "Inefficient Credit Booms," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(3), pages 809-833.
    5. Tobias Eckernkemper, 2018. "Modeling Systemic Risk: Time-Varying Tail Dependence When Forecasting Marginal Expected Shortfall," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 63-117.
    6. Bernal, Oscar & Gnabo, Jean-Yves & Guilmin, Grégory, 2014. "Assessing the contribution of banks, insurance and other financial services to systemic risk," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 270-287.
    7. Laeven, Luc & Ratnovski, Lev & Tong, Hui, 2016. "Bank size, capital, and systemic risk: Some international evidence," Journal of Banking & Finance, Elsevier, vol. 69(S1), pages 25-34.
    8. J. David Cummins & Mary A. Weiss, 2013. "Systemic Risk and Regulation of the U.S. Insurance Industry," NFI Policy Briefs 2013-PB-02, Indiana State University, Scott College of Business, Networks Financial Institute.
    9. Ling Hu, 2006. "Dependence patterns across financial markets: a mixed copula approach," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 717-729.
    10. Girardi, Giulio & Tolga Ergün, A., 2013. "Systemic risk measurement: Multivariate GARCH estimation of CoVaR," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3169-3180.
    11. Mathias Manguzvane & John Weirstrass Muteba Mwamba, 2019. "Modelling systemic risk in the South African banking sector using CoVaR," International Review of Applied Economics, Taylor & Francis Journals, vol. 33(5), pages 624-641, September.
    12. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiang, Kunliang & Ye, Wuyi, 2022. "Does the asymmetric dependence volatility affect risk spillovers between the crude oil market and BRICS stock markets?," Economic Modelling, Elsevier, vol. 117(C).
    2. Kanga, Désiré & Soumaré, Issouf & Amenounvé, Edoh, 2023. "Can corporate financing through the stock market create systemic risk? Evidence from the BRVM securities market," Emerging Markets Review, Elsevier, vol. 55(C).
    3. Adeabah, David & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar & Hammoudeh, Shawkat, 2023. "How far have we come and where should we go after 30+ years of research on Africa's emerging financial markets? A systematic review and a bibliometric network analysis," Emerging Markets Review, Elsevier, vol. 55(C).

    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. Rahman, Md Lutfur & Troster, Victor & Uddin, Gazi Salah & Yahya, Muhammad, 2022. "Systemic risk contribution of banks and non-bank financial institutions across frequencies: The Australian experience," International Review of Financial Analysis, Elsevier, vol. 79(C).
    2. Reboredo, Juan C. & Ugolini, Andrea, 2015. "A vine-copula conditional value-at-risk approach to systemic sovereign debt risk for the financial sector," The North American Journal of Economics and Finance, Elsevier, vol. 32(C), pages 98-123.
    3. Wang, Bo & Xiao, Yang, 2023. "Risk spillovers from China's and the US stock markets during high-volatility periods: Evidence from East Asianstock markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    4. Yufei Cao, 2021. "Modeling the dependence structure and systemic risk of all listed insurance companies in the Chinese insurance market," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 24(4), pages 367-399, December.
    5. Tian, Maoxi & Alshater, Muneer M. & Yoon, Seong-Min, 2022. "Dynamic risk spillovers from oil to stock markets: Fresh evidence from GARCH copula quantile regression-based CoVaR model," Energy Economics, Elsevier, vol. 115(C).
    6. Tian, Maoxi & Guo, Fei & Niu, Rong, 2022. "Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    7. Xiao, Yang, 2020. "The risk spillovers from the Chinese stock market to major East Asian stock markets: A MSGARCH-EVT-copula approach," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 173-186.
    8. Warshaw, Evan, 2019. "Extreme dependence and risk spillovers across north american equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 237-251.
    9. Varotto, Simone & Zhao, Lei, 2018. "Systemic risk and bank size," Journal of International Money and Finance, Elsevier, vol. 82(C), pages 45-70.
    10. Zhang, Weiping & Zhuang, Xintian & Wang, Jian & Lu, Yang, 2020. "Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    11. Bernal, Oscar & Gnabo, Jean-Yves & Guilmin, Grégory, 2014. "Assessing the contribution of banks, insurance and other financial services to systemic risk," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 270-287.
    12. Liu, Xiaochun, 2017. "Measuring systemic risk with regime switching in tails," Economic Modelling, Elsevier, vol. 67(C), pages 55-72.
    13. Fang, Libing & Chen, Baizhu & Yu, Honghai & Qian, Yichuo, 2018. "Identifying systemic important markets from a global perspective: Using the ADCC ΔCoVaR approach with skewed-t distribution," Finance Research Letters, Elsevier, vol. 24(C), pages 137-144.
    14. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Risk spillovers and diversification between oil and non-ferrous metals during bear and bull market states," Resources Policy, Elsevier, vol. 72(C).
    15. Yangmin Ke & Chongguang Li & Andrew M. McKenzie & Ping Liu, 2019. "Risk Transmission between Chinese and U.S. Agricultural Commodity Futures Markets—A CoVaR Approach," Sustainability, MDPI, vol. 11(1), pages 1-18, January.
    16. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.
    17. Karmakar, Madhusudan, 2017. "Dependence structure and portfolio risk in Indian foreign exchange market: A GARCH-EVT-Copula approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 64(C), pages 275-291.
    18. Tong, Bin & Diao, Xundi & Wu, Chongfeng, 2015. "Modeling asymmetric and dynamic dependence of overnight and daytime returns: An empirical evidence from China Banking Sector," Economic Modelling, Elsevier, vol. 51(C), pages 366-382.
    19. Miguel Rivera-Castro & Andrea Ugolini & Juan Arismendi Z, 2016. "Tail Systemic Risk And Banking Network Contagion: Evidence From the Brazilian Banking System," ICMA Centre Discussion Papers in Finance icma-dp2016-05, Henley Business School, University of Reading.
    20. Ferreiro Javier Ojea, 2019. "Structural change in the link between oil and the European stock market: implications for risk management," Dependence Modeling, De Gruyter, vol. 7(1), pages 53-125, January.

    More about this item

    Keywords

    Banks; Copula; Generalized autoregressive score model; Insurers; South Africa; Systemically important;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

    Statistics

    Access and download statistics

    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:spr:empeco:v:59:y:2020:i:4:d:10.1007_s00181-019-01695-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.