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An empirical comparison of some experimental designs for the valuation of large variable annuity portfolios

Author

Listed:
  • Gan Guojun

    (Department of Mathematics, University of Connecticut)

  • Valdez Emiliano A.

    (Department of Mathematics, University of Connecticut)

Abstract

Variable annuities contain complex guarantees, whose fair market value cannot be calculated in closed form. To value the guarantees, insurance companies rely heavily on Monte Carlo simulation, which is extremely computationally demanding for large portfolios of variable annuity policies. Metamodeling approaches have been proposed to address these computational issues. An important step of metamodeling approaches is the experimental design that selects a small number of representative variable annuity policies for building metamodels. In this paper, we compare empirically several multivariate experimental design methods for the GB2 regression model, which has been recently discovered to be an attractive model to estimate the fair market value of variable annuity guarantees. Among the experimental design methods examined, we found that the data clustering method and the conditional Latin hypercube sampling method produce the most accurate results.

Suggested Citation

  • Gan Guojun & Valdez Emiliano A., 2016. "An empirical comparison of some experimental designs for the valuation of large variable annuity portfolios," Dependence Modeling, De Gruyter, vol. 4(1), pages 1-19, December.
    • Handle: RePEc:vrs:demode:v:4:y:2016:i:1:p:19:n:22
    DOI: 10.1515/demo-2016-0022
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    Cited by:

    1. Jiang, Ruihong & Saunders, David & Weng, Chengguo, 2023. "Two-phase selection of representative contracts for valuation of large variable annuity portfolios," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 293-309.
    2. Shuang Yin & Guojun Gan & Emiliano A. Valdez & Jeyaraj Vadiveloo, 2021. "Applications of Clustering with Mixed Type Data in Life Insurance," Risks, MDPI, vol. 9(3), pages 1-19, March.
    3. Guojun Gan, 2018. "Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions," Risks, MDPI, vol. 6(3), pages 1-19, July.
    4. Daniel Doyle & Chris Groendyke, 2018. "Using Neural Networks to Price and Hedge Variable Annuity Guarantees," Risks, MDPI, vol. 7(1), pages 1-19, December.
    5. Nicholas Bett & Juma Kasozi & Daniel Ruturwa, 2022. "Temporal Clustering of the Causes of Death for Mortality Modelling," Risks, MDPI, vol. 10(5), pages 1-34, May.

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