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An optimal stochastic control framework for determining the cost of hedging of variable annuities

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  • Forsyth, Peter
  • Vetzal, Kenneth

Abstract

An implicit partial differential equation (PDE) method is used to determine the cost of hedging for a Guaranteed Lifelong Withdrawal Benefit (GLWB) variable annuity contract. In the basic setting, the underlying risky asset is assumed to evolve according to geometric Brownian motion, but this is generalised to the case of a Markov regime switching process. A similarity transformation is used to reduce a pricing problem with K regimes to the solution of K coupled one dimensional PDEs, resulting in a considerable gain in computational efficiency. The methodology developed is flexible in the sense that it can calculate the cost of hedging for a variety of different withdrawal strategies by investors. Cases considered here include both optimal withdrawal strategies (i.e. strategies which generate the highest possible cost of hedging for the insurer) and sub-optimal withdrawal strategies in which the policy holder׳s decisions depend on the moneyness of the embedded options. Numerical results are presented which demonstrate the sensitivity of the cost of hedging (given the withdrawal specification) to various economic and contractual assumptions.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:dyncon:v:44:y:2014:i:c:p:29-53
    DOI: 10.1016/j.jedc.2014.04.005
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    Cited by:

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    3. Tak Kuen Siu & Robert J. Elliott, 2019. "Hedging Options In A Doubly Markov-Modulated Financial Market Via Stochastic Flows," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(08), pages 1-41, December.
    4. 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.
    5. Pavel V. Shevchenko & Xiaolin Luo, 2016. "A Unified Pricing of Variable Annuity Guarantees under the Optimal Stochastic Control Framework," Risks, MDPI, vol. 4(3), pages 1-31, July.
    6. Shevchenko, Pavel V. & Luo, Xiaolin, 2017. "Valuation of variable annuities with Guaranteed Minimum Withdrawal Benefit under stochastic interest rate," Insurance: Mathematics and Economics, Elsevier, vol. 76(C), pages 104-117.
    7. 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.
    8. Ankush Agarwal & Christian-Oliver Ewald & Yongjie Wang, 2023. "Hedging longevity risk in defined contribution pension schemes," Computational Management Science, Springer, vol. 20(1), pages 1-34, December.
    9. Pavel V. Shevchenko & Xiaolin Luo, 2016. "A unified pricing of variable annuity guarantees under the optimal stochastic control framework," Papers 1605.00339, arXiv.org.
    10. Jin Sun & Pavel V. Shevchenko & Man Chung Fung, 2018. "The Impact of Management Fees on the Pricing of Variable Annuity Guarantees," Risks, MDPI, vol. 6(3), pages 1-20, September.
    11. Runhuan Feng & Jan Vecer, 2017. "Risk based capital for guaranteed minimum withdrawal benefit," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 471-478, March.
    12. 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.
    13. Dong, Bing & Xu, Wei & Sevic, Aleksandar & Sevic, Zeljko, 2020. "Efficient willow tree method for variable annuities valuation and risk management☆," International Review of Financial Analysis, Elsevier, vol. 68(C).
    14. Nitu Sharma & S. Dharmaraja & Viswanathan Arunachalam, 2021. "A Time Series Framework for Pricing Guaranteed Lifelong Withdrawal Benefit," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1225-1261, December.
    15. Niu, Shilei & Insley, Margaret, 2016. "An options pricing approach to ramping rate restrictions at hydro power plants," Journal of Economic Dynamics and Control, Elsevier, vol. 63(C), pages 25-52.
    16. Parsiad Azimzadeh & Peter A. Forsyth, 2015. "The existence of optimal bang-bang controls for GMxB contracts," Papers 1502.05743, arXiv.org, revised Nov 2015.
    17. Goudenège, Ludovic & Molent, Andrea & Zanette, Antonino, 2016. "Pricing and hedging GLWB in the Heston and in the Black–Scholes with stochastic interest rate models," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 38-57.
    18. Forsyth, Peter A., 2020. "Optimal dynamic asset allocation for DC plan accumulation/decumulation: Ambition-CVAR," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 230-245.
    19. Moenig, Thorsten, 2021. "Variable annuities: Market incompleteness and policyholder behavior," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 63-78.
    20. Ludovic Goudenege & Andrea Molent & Antonino Zanette, 2015. "Pricing and Hedging GLWB in the Heston and in the Black-Scholes with Stochastic Interest Rate Models," Papers 1509.02686, arXiv.org.
    21. Andrea Molent, 2019. "Taxation of a GMWB Variable Annuity in a Stochastic Interest Rate Model," Papers 1901.11296, arXiv.org, revised May 2020.

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    More about this item

    Keywords

    Optimal control; GLWB pricing; PDE approach; Regime switching; No-arbitrage; Withdrawal strategies;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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