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The price determinants of contingent convertible bonds

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  • Zeitsch, Peter J.
  • Davis, Tom P.

Abstract

The relationships between contingent convertible (CoCo) bonds and their underlying equities, credit default swap spreads (CDS), interest rates, implied volatilities and foreign exchange rates are studied. Starting with the dynamic correlation of the DCC-GARCH method, it is found that CoCo bonds are most highly correlated to CDS. By constructing the minimum spanning tree of the resulting correlations, the primary link to CDS is confirmed. Implied volatility is found to be a secondary to tertiary link, alternating in importance with equities. Interest rates and FX have little impact.

Suggested Citation

  • Zeitsch, Peter J. & Davis, Tom P., 2021. "The price determinants of contingent convertible bonds," Finance Research Letters, Elsevier, vol. 43(C).
  • Handle: RePEc:eee:finlet:v:43:y:2021:i:c:s1544612321000969
    DOI: 10.1016/j.frl.2021.102015
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    References listed on IDEAS

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    1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    2. Guillermo J. Ortega & David Matesanz, 2006. "Cross-Country Hierarchical Structure And Currency Crises," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 333-341.
    3. León, Carlos & Leiton, Karen & Pérez, Jhonatan, 2014. "Extracting the sovereigns’ CDS market hierarchy: A correlation-filtering approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 407-420.
    4. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    5. Tumminello, Michele & Lillo, Fabrizio & Mantegna, Rosario N., 2010. "Correlation, hierarchies, and networks in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 75(1), pages 40-58, July.
    6. Peter J. Zeitsch, 2017. "Capital Structure Arbitrage under a Risk-Neutral Calibration," JRFM, MDPI, vol. 10(1), pages 1-23, January.
    7. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
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    Cited by:

    1. Liwei Jin & Xianghui Yuan & Li Peiran & Hailun Xu & Feng Lian, 2023. "Option features and price discovery in convertible bonds," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(3), pages 384-403, March.

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