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A characterization of CAT bond performance indices

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  • Trottier, Denis-Alexandre
  • Lai, Van Son
  • Godin, Frédéric

Abstract

Although several works have highlighted the diversification benefits of catastrophe (CAT) bond funds as well as the attracting returns they offer, there is a lack in the literature regarding what econometric models are suitable to predict the risks of such funds. This note contributes by offering such a statistical description of the dynamics of CAT bond indices total returns series. The approach is based on a regime-switching model that parsimoniously accounts for the leptokurtosis, skewness, and autocorrelation of returns, as well as for (G)ARCH effects, seasonality, and the sudden impact of natural disasters. Estimation and specification testing is carried out for four weekly indices tracking the performance of different CAT bond sectors; this allows identifying several salient stylized features for the returns dynamics of this asset class.

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  • Trottier, Denis-Alexandre & Lai, Van Son & Godin, Frédéric, 2019. "A characterization of CAT bond performance indices," Finance Research Letters, Elsevier, vol. 28(C), pages 431-437.
    • Handle: RePEc:eee:finlet:v:28:y:2019:i:c:p:431-437
    DOI: 10.1016/j.frl.2018.06.016
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    1. Getmansky, Mila & Lo, Andrew W. & Makarov, Igor, 2004. "An econometric model of serial correlation and illiquidity in hedge fund returns," Journal of Financial Economics, Elsevier, vol. 74(3), pages 529-609, December.
    2. Weron, R & Bierbrauer, M & Trück, S, 2004. "Modeling electricity prices: jump diffusion and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 39-48.
    3. Andrew Ang & Allan Timmermann, 2012. "Regime Changes and Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337, October.
    4. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    5. Braun, Alexander & Ben Ammar, Semir & Eling, Martin, 2019. "Asset pricing and extreme event risk: Common factors in ILS fund returns," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 59-78.
    6. Mary Hardy, 2001. "A Regime-Switching Model of Long-Term Stock Returns," North American Actuarial Journal, Taylor & Francis Journals, vol. 5(2), pages 41-53.
    7. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(3), pages 239-270, July.
    8. Markus Haas, 2004. "A New Approach to Markov-Switching GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 493-530.
    9. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    10. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    11. Daniel R. Smith, 2008. "Evaluating Specification Tests for Markov‐Switching Time‐Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(4), pages 629-652, July.
    12. Trottier, Denis-Alexandre & Ardia, David, 2016. "Moments of standardized Fernandez–Steel skewed distributions: Applications to the estimation of GARCH-type models," Finance Research Letters, Elsevier, vol. 18(C), pages 311-316.
    13. Peter Carayannopoulos & M Fabricio Perez, 2015. "Diversification through Catastrophe Bonds: Lessons from the Subprime Financial Crisis," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 40(1), pages 1-28, January.
    14. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
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    Cited by:

    1. Karl Demers‐Bélanger & Van Son Lai, 2020. "Diversification benefits of cat bonds: An in‐depth examination," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 29(5), pages 165-228, December.
    2. Peter Carayannopoulos & Olga Kanj & M. Fabricio Perez, 2022. "Pricing dynamics in the market for catastrophe bonds," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(1), pages 172-202, January.
    3. Drobetz, Wolfgang & Schröder, Henning & Tegtmeier, Lars, 2020. "The role of catastrophe bonds in an international multi-asset portfolio: Diversifier, hedge, or safe haven?," Finance Research Letters, Elsevier, vol. 33(C).

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