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Calculating Variable Annuity Liability €Œgreeks†Using Monte Carlo Simulation

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  • Cathcart, Mark J.
  • Lok, Hsiao Yen
  • McNeil, Alexander J.
  • Morrison, Steven

Abstract

The implementation of hedging strategies for variable annuity products requires the calculation of market risk sensitivities (or “Greeks†). The complex, path-dependent nature of these products means that these sensitivities are typically estimated by Monte Carlo methods. Standard market practice is to use a “bump and revalue†method in which sensitivities are approximated by finite differences. As well as requiring multiple valuations of the product, this approach is often unreliable for higher-order Greeks, such as gamma, and alternative pathwise (PW) and likelihood-ratio estimators should be preferred. This paper considers a stylized guaranteed minimum withdrawal benefit product in which the reference equity index follows a Heston stochastic volatility model in a stochastic interest rate environment. The complete set of first-order sensitivities with respect to index value, volatility and interest rate and the most important second-order sensitivities are calculated using PW, likelihood-ratio and mixed methods. It is observed that the PW method delivers the best estimates of first-order sensitivities while mixed estimation methods deliver considerably more accurate estimates of second-order sensitivities; moreover there are significant computational gains involved in using PW and mixed estimators rather than simple BnR estimators when many Greeks have to be calculated.

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  • Cathcart, Mark J. & Lok, Hsiao Yen & McNeil, Alexander J. & Morrison, Steven, 2015. "Calculating Variable Annuity Liability €Œgreeks†Using Monte Carlo Simulation," ASTIN Bulletin, Cambridge University Press, vol. 45(2), pages 239-266, May.
  • Handle: RePEc:cup:astinb:v:45:y:2015:i:02:p:239-266_00
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    Cited by:

    1. 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.
    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. Bauer Jan, 2020. "Hedging of Variable Annuities under Basis Risk," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 14(2), pages 1-34, July.
    4. 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.
    5. Maciej Augustyniak & Mathieu Boudreault, 2017. "Mitigating Interest Rate Risk in Variable Annuities: An Analysis of Hedging Effectiveness under Model Risk," North American Actuarial Journal, Taylor & Francis Journals, vol. 21(4), pages 502-525, October.

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