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Phase-Type Models in Life Insurance: Fitting and Valuation of Equity-Linked Benefits

Author

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  • Søren Asmussen

    (Department of Mathematics, Aarhus University, 8000 Aarhus, Denmark)

  • Patrick J. Laub

    (Institut de Science Financière et d’Assurances, Université Lyon 1, 69007 Lyon, France)

  • Hailiang Yang

    (Department of Statistics & Actuarial Science, Hong Kong University, Hong Kong 999077, China)

Abstract

Phase-type (PH) distributions are defined as distributions of lifetimes of finite continuous-time Markov processes. Their traditional applications are in queueing, insurance risk, and reliability, but more recently, also in finance and, though to a lesser extent, to life and health insurance. The advantage is that PH distributions form a dense class and that problems having explicit solutions for exponential distributions typically become computationally tractable under PH assumptions. In the first part of this paper, fitting of PH distributions to human lifetimes is considered. The class of generalized Coxian distributions is given special attention. In part, some new software is developed. In the second part, pricing of life insurance products such as guaranteed minimum death benefit and high-water benefit is treated for the case where the lifetime distribution is approximated by a PH distribution and the underlying asset price process is described by a jump diffusion with PH jumps. The expressions are typically explicit in terms of matrix-exponentials involving two matrices closely related to the Wiener-Hopf factorization, for which recently, a Lévy process version has been developed for a PH horizon. The computational power of the method of the approach is illustrated via a number of numerical examples.

Suggested Citation

  • Søren Asmussen & Patrick J. Laub & Hailiang Yang, 2019. "Phase-Type Models in Life Insurance: Fitting and Valuation of Equity-Linked Benefits," Risks, MDPI, vol. 7(1), pages 1-22, February.
  • Handle: RePEc:gam:jrisks:v:7:y:2019:i:1:p:17-:d:204956
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    References listed on IDEAS

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    Cited by:

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    5. Albrecher, Hansjörg & Bladt, Martin & Bladt, Mogens & Yslas, Jorge, 2022. "Mortality modeling and regression with matrix distributions," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 68-87.
    6. Albrecher Hansjörg & Bladt Martin & Müller Alaric J. A., 2023. "Joint lifetime modeling with matrix distributions," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-22, January.
    7. Deelstra, Griselda & Hieber, Peter, 2023. "Randomization and the valuation of guaranteed minimum death benefits," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1218-1236.
    8. Asmussen, Søren & Bladt, Mogens, 2022. "Moments and polynomial expansions in discrete matrix-analytic models," Stochastic Processes and their Applications, Elsevier, vol. 150(C), pages 1165-1188.
    9. Jevgenijs Ivanovs, 2021. "On scale functions for Lévy processes with negative phase-type jumps," Queueing Systems: Theory and Applications, Springer, vol. 98(1), pages 3-19, June.
    10. Boquan Cheng & Rogemar Mamon, 2023. "A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 142-187, January.

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