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Two-phase selection of representative contracts for valuation of large variable annuity portfolios

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  • Jiang, Ruihong
  • Saunders, David
  • Weng, Chengguo

Abstract

A computationally appealing methodology for the valuation of large variable annuities portfolios is a metamodelling framework that evaluates a small set of representative contracts, fits a predictive model based on these computed values, and then extrapolates the model to estimate the values of the remaining contracts. This paper proposes a new two-phase procedure for selecting representative contracts. The representatives from the first phase are determined using contract attributes as in existing metamodelling approaches, but those in the second phase are chosen by utilizing the information contained in the values of the representatives from the first phase. Two numerical studies confirm that our two-phase selection procedure improves upon conventional approaches from the existing literature.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:insuma:v:113:y:2023:i:c:p:293-309
    DOI: 10.1016/j.insmatheco.2023.08.009
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    References listed on IDEAS

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    1. Gweon, Hyukjun & Li, Shu & Mamon, Rogemar, 2020. "An Effective Bias-Corrected Bagging Method For The Valuation Of Large Variable Annuity Portfolios," ASTIN Bulletin, Cambridge University Press, vol. 50(3), pages 853-871, September.
    2. Seyed Amir Hejazi & Kenneth R. Jackson, 2016. "A Neural Network Approach to Efficient Valuation of Large Portfolios of Variable Annuities," Papers 1606.07831, arXiv.org.
    3. Kai Liu & Ken Seng Tan, 2021. "Real-Time Valuation of Large Variable Annuity Portfolios: A Green Mesh Approach," North American Actuarial Journal, Taylor & Francis Journals, vol. 25(3), pages 313-333, July.
    4. Bacinello, Anna Rita & Millossovich, Pietro & Olivieri, Annamaria & Pitacco, Ermanno, 2011. "Variable annuities: A unifying valuation approach," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 285-297.
    5. Mary Hardy, 2000. "Hedging and Reserving for Single-Premium Segregated Fund Contracts," North American Actuarial Journal, Taylor & Francis Journals, vol. 4(2), pages 63-74.
    6. Huang, Yiming & Mamon, Rogemar & Xiong, Heng, 2022. "Valuing guaranteed minimum accumulation benefits by a change of numéraire approach," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 1-26.
    7. Yang, Sharon S. & Dai, Tian-Shyr, 2013. "A flexible tree for evaluating guaranteed minimum withdrawal benefits under deferred life annuity contracts with various provisions," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 231-242.
    8. Zhiyi Shen & Chengguo Weng, 2019. "A Backward Simulation Method for Stochastic Optimal Control Problems," Papers 1901.06715, arXiv.org.
    9. Yao Tung Huang & Yue Kuen Kwok, 2016. "Regression-based Monte Carlo methods for stochastic control models: variable annuities with lifelong guarantees," Quantitative Finance, Taylor & Francis Journals, vol. 16(6), pages 905-928, June.
    10. 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.
    11. Xu, Wei & Chen, Yuehuan & Coleman, Conrad & Coleman, Thomas F., 2018. "Moment matching machine learning methods for risk management of large variable annuity portfolios," Journal of Economic Dynamics and Control, Elsevier, vol. 87(C), pages 1-20.
    12. Gan, Guojun & Lin, X. Sheldon, 2015. "Valuation of large variable annuity portfolios under nested simulation: A functional data approach," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 138-150.
    13. Guojun Gan, 2018. "Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions," Risks, MDPI, vol. 6(3), pages 1-19, July.
    14. Gan, Guojun, 2013. "Application of data clustering and machine learning in variable annuity valuation," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 795-801.
    15. Gweon, Hyukjun & Li, Shu, 2021. "Batch mode active learning framework and its application on valuing large variable annuity portfolios," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 105-115.
    16. Bauer, Daniel & Kling, Alexander & Russ, Jochen, 2008. "A Universal Pricing Framework for Guaranteed Minimum Benefits in Variable Annuities 1," ASTIN Bulletin, Cambridge University Press, vol. 38(2), pages 621-651, November.
    17. Brennan, Michael J. & Schwartz, Eduardo S., 1976. "The pricing of equity-linked life insurance policies with an asset value guarantee," Journal of Financial Economics, Elsevier, vol. 3(3), pages 195-213, June.
    18. Zhiyi Shen & Chengguo Weng, 2020. "Pricing bounds and bang-bang analysis of the Polaris variable annuities," Quantitative Finance, Taylor & Francis Journals, vol. 20(1), pages 147-171, January.
    19. Lin, X. Sheldon & Yang, Shuai, 2020. "Efficient Dynamic Hedging For Large Variable Annuity Portfolios With Multiple Underlying Assets," ASTIN Bulletin, Cambridge University Press, vol. 50(3), pages 913-957, September.
    20. Lin, X. Sheldon & Yang, Shuai, 2020. "Fast and efficient nested simulation for large variable annuity portfolios: A surrogate modeling approach," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 85-103.
    21. Bauer, Daniel & Reuss, Andreas & Singer, Daniela, 2012. "On the Calculation of the Solvency Capital Requirement Based on Nested Simulations," ASTIN Bulletin, Cambridge University Press, vol. 42(2), pages 453-499, November.
    22. Guojun Gan & X. Sheldon Lin, 2017. "Efficient Greek Calculation of Variable Annuity Portfolios for Dynamic Hedging: A Two-Level Metamodeling Approach," North American Actuarial Journal, Taylor & Francis Journals, vol. 21(2), pages 161-177, April.
    23. Boyle, Phelim P. & Hardy, Mary R., 1997. "Reserving for maturity guarantees: Two approaches," Insurance: Mathematics and Economics, Elsevier, vol. 21(2), pages 113-127, November.
    24. Guojun Gan & Emiliano A. Valdez, 2020. "Data Clustering with Actuarial Applications," North American Actuarial Journal, Taylor & Francis Journals, vol. 24(2), pages 168-186, April.
    25. Guojun Gan & Emiliano A. Valdez, 2018. "Regression Modeling for the Valuation of Large Variable Annuity Portfolios," North American Actuarial Journal, Taylor & Francis Journals, vol. 22(1), pages 40-54, January.
    26. Bacinello, Anna Rita & Ortu, Fulvio, 1993. "Pricing equity-linked life insurance with endogenous minimum guarantees : A corrigendum," Insurance: Mathematics and Economics, Elsevier, vol. 13(3), pages 303-304, December.
    27. Gan Guojun & Valdez Emiliano A., 2017. "Valuation of large variable annuity portfolios: Monte Carlo simulation and synthetic datasets," Dependence Modeling, De Gruyter, vol. 5(1), pages 354-374, December.
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    More about this item

    Keywords

    Variable annuity portfolio; Clustering; Kriging; Conditional k-means; Mini-batch k-means; Two-phase selection;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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