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A general class of distortion operators for pricing contingent claims with applications to CAT bonds

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

Abstract

The current paper provides a general approach to construct distortion operators that can price financial and insurance risks. Our approach generalizes the (Wang 2000) transform and recovers multiple distortions proposed in the literature as particular cases. This approach enables designing distortions that are consistent with various pricing principles used in finance and insurance such as no-arbitrage models, equilibrium models and actuarial premium calculation principles. Such distortions allow for the incorporation of risk-aversion, distribution features (e.g. skewness and kurtosis) and other considerations that are relevant to price contingent claims. The pricing performance of multiple distortions obtained through our approach is assessed on CAT bonds data. The current paper is the first to provide evidence that jump-diffusion models are appropriate for CAT bonds pricing, and that natural disaster aversion impacts empirical prices. A simpler distortion based on a distribution mixture is finally proposed for CAT bonds pricing to facilitate the implementation.

Suggested Citation

  • Frédéric Godin & Van Son Lai & Denis-Alexandre Trottier, 2019. "A general class of distortion operators for pricing contingent claims with applications to CAT bonds," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2019(7), pages 558-584, August.
    • Handle: RePEc:taf:sactxx:v:2019:y:2019:i:7:p:558-584
    DOI: 10.1080/03461238.2019.1581837
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    3. Sukono & Hafizan Juahir & Riza Andrian Ibrahim & Moch Panji Agung Saputra & Yuyun Hidayat & Igif Gimin Prihanto, 2022. "Application of Compound Poisson Process in Pricing Catastrophe Bonds: A Systematic Literature Review," Mathematics, MDPI, vol. 10(15), pages 1-19, July.
    4. Holly Brannelly & Andrea Macrina & Gareth W. Peters, 2021. "Stochastic measure distortions induced by quantile processes for risk quantification and valuation," Papers 2201.02045, arXiv.org.

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