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Modeling partial Greeks of variable annuities with dependence

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  • Gan, Guojun
  • Valdez, Emiliano A.

Abstract

Dynamic hedging used to mitigate the financial risks associated with large portfolios of variable annuities requires calculating partial dollar deltas on major market indices. Metamodeling approaches have been proposed in the past few years to address the computational issues related to the calculation of partial dollar deltas. In this paper, we investigate whether the additional complication of modeling the dependence between the partial dollar deltas improves the accuracy of the metamodeling approaches. We use several copulas to model the dependence structures of the partial dollar deltas and conduct numerical experiments to compare different metamodels. Despite the evidence of strong dependence in the estimated models, our numerical results show that modeling the dependence structures in the metamodels does not improve the accuracy of the estimations at the portfolio level. This is because the dependence between the partial dollar deltas is well captured by the covariates used in the marginal models. This finding suggests that we should focus more on marginal models than specifying the dependence structure explicitly.

Suggested Citation

  • Gan, Guojun & Valdez, Emiliano A., 2017. "Modeling partial Greeks of variable annuities with dependence," Insurance: Mathematics and Economics, Elsevier, vol. 76(C), pages 118-134.
  • Handle: RePEc:eee:insuma:v:76:y:2017:i:c:p:118-134
    DOI: 10.1016/j.insmatheco.2017.07.006
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    References listed on IDEAS

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    1. 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.
    2. Garcia, René & Tsafack, Georges, 2011. "Dependence structure and extreme comovements in international equity and bond markets," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1954-1970, August.
    3. Kleinow, Torsten & Willder, Mark, 2007. "The effect of management discretion on hedging and fair valuation of participating policies with maturity guarantees," Insurance: Mathematics and Economics, Elsevier, vol. 40(3), pages 445-458, May.
    4. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    5. Hejazi, Seyed Amir & Jackson, Kenneth R., 2016. "A neural network approach to efficient valuation of large portfolios of variable annuities," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 169-181.
    6. Andrew Ng & Johnny Li, 2013. "Pricing and Hedging Variable Annuity Guarantees with Multiasset Stochastic Investment Models," North American Actuarial Journal, Taylor & Francis Journals, vol. 17(1), pages 41-62.
    7. Ledlie, M. C. & Corry, D. P. & Finkelstein, G. S. & Ritchie, A. J. & Su, K. & Wilson, D. C. E., 2008. "Variable Annuities," British Actuarial Journal, Cambridge University Press, vol. 14(2), pages 327-389, July.
    8. Melnikov, Alexander & Tong, Shuo, 2014. "Quantile hedging on equity-linked life insurance contracts with transaction costs," Insurance: Mathematics and Economics, Elsevier, vol. 58(C), pages 77-88.
    9. 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.
    10. Li, Jing & Szimayer, Alexander, 2011. "The uncertain mortality intensity framework: Pricing and hedging unit-linked life insurance contracts," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 471-486.
    11. 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.
    12. Møller, Thomas, 1998. "Risk-Minimizing Hedging Strategies for Unit-Linked Life Insurance Contracts," ASTIN Bulletin, Cambridge University Press, vol. 28(1), pages 17-47, May.
    13. Heath Windcliff & Martin Le Roux & Peter Forsyth & Kenneth Vetzal, 2002. "Understanding the Behavior and Hedging of Segregated Funds Offering the Reset Feature," North American Actuarial Journal, Taylor & Francis Journals, vol. 6(2), pages 107-124.
    14. Edward Frees & Emiliano Valdez, 1998. "Understanding Relationships Using Copulas," North American Actuarial Journal, Taylor & Francis Journals, vol. 2(1), pages 1-25.
    15. Ling Hu, 2006. "Dependence patterns across financial markets: a mixed copula approach," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 717-729.
    16. Forsyth, Peter & Vetzal, Kenneth, 2014. "An optimal stochastic control framework for determining the cost of hedging of variable annuities," Journal of Economic Dynamics and Control, Elsevier, vol. 44(C), pages 29-53.
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    Cited by:

    1. 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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