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Dependence Structure of Insurance Credit Default Swaps

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  • Mudiangombe, Benjamin
  • Muteba Mwamba, John Weirstrass

Abstract

We examine the dependence structure of insurance credit default swap (CDS) indices in the pairs of markets of the United Kingdom (UK), Eurozone (EU) and United States (US) insurance industries during the period of January 2004 to October 2018. We applied the Archimedean Clayton copula to model the lower tail and the Gumbel copula to model the upper tail of the empirical distributions. The empirical results show a significant dependence structure for both constant and time-varying copulas, implying the co-movement in the pairs of markets during the study period, influencing the contagion risk and showing strong dependence among Markets. The highest tail dependence and positive adjustment parameters seen in crisis and debt-crisis in the lower regime explains the link between these markets. The crucial findings show confirmation of asymmetric tail dependence proposing the propagation of risks of default among UK, EU and US markets. The conditional tail of the time-varying dependence structure explains the behaviour of dependence better than the constant level. This finding is robust when measuring the evolution of the dependence structure over time. The results are consistent for risk managers and investors to select the portfolio investment in different markets during stress period.

Suggested Citation

  • Mudiangombe, Benjamin & Muteba Mwamba, John Weirstrass, 2019. "Dependence Structure of Insurance Credit Default Swaps," MPRA Paper 97335, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:97335
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    References listed on IDEAS

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    1. Creal, Drew D. & Tsay, Ruey S., 2015. "High dimensional dynamic stochastic copula models," Journal of Econometrics, Elsevier, vol. 189(2), pages 335-345.
    2. da Silva, Paulo Pereira & Rebelo, Paulo Tomaz & Afonso, Cristina, 2014. "Tail dependence of financial stocks and CDS markets: Evidence using copula methods and simulation-based inference," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW), vol. 8, pages 1-27.
    3. Brechmann, Eike C. & Hendrich, Katharina & Czado, Claudia, 2013. "Conditional copula simulation for systemic risk stress testing," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 722-732.
    4. Dias, Alexandra & Embrechts, Paul, 2010. "Modeling exchange rate dependence dynamics at different time horizons," Journal of International Money and Finance, Elsevier, vol. 29(8), pages 1687-1705, December.
    5. Fathi Abid & Nader Naifar, 2005. "The Impact Of Stock Returns Volatility On Credit Default Swap Rates: A Copula Study," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(08), pages 1135-1155.
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    Cited by:

    1. Mubenga-Tshitaka, Jean-Luc & Muteba Mwamba, John W. & Dikgang, Johane & Gelo, Dambala, 2021. "Risk spillover between climate variables and the agricultural commodity market in East Africa," EconStor Preprints 243160, ZBW - Leibniz Information Centre for Economics.

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    More about this item

    Keywords

    Dependence structure; Insurance credit default swaps; Constant and Time-varying Copulas;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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