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Efficient Simulation Designs for Valuation of Large Variable Annuity Portfolios

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  • Ben Mingbin Feng
  • Zhenni Tan
  • Jiayi Zheng

Abstract

The valuation of large variable annuity portfolios is an important enterprise risk management task but is computationally challenging due to the need for simulation. Existing methods in the literature only use simple experimental designs with significant room for improvement. This article identifies three major components in an efficient valuation framework. In addition, we propose optimal experimental designs and provides analytical insights for each component. Our numerical results show that our proposal achieves significantly higher accuracy than state-of-the-art alternatives without requiring any additional computational resource.

Suggested Citation

  • Ben Mingbin Feng & Zhenni Tan & Jiayi Zheng, 2020. "Efficient Simulation Designs for Valuation of Large Variable Annuity Portfolios," North American Actuarial Journal, Taylor & Francis Journals, vol. 24(2), pages 275-289, April.
  • Handle: RePEc:taf:uaajxx:v:24:y:2020:i:2:p:275-289
    DOI: 10.1080/10920277.2019.1685394
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    Cited by:

    1. Thorsten Moenig, 2021. "Efficient valuation of variable annuity portfolios with dynamic programming," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(4), pages 1023-1055, December.
    2. Dang, Ou & Feng, Mingbin & Hardy, Mary R., 2023. "Two-stage nested simulation of tail risk measurement: A likelihood ratio approach," Insurance: Mathematics and Economics, Elsevier, vol. 108(C), pages 1-24.
    3. Wing Fung Chong & Haoen Cui & Yuxuan Li, 2021. "Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning," Papers 2107.03340, arXiv.org, revised Oct 2022.

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