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Longevity Greeks: What Do Insurers and Capital Market Investors Need to Know?

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  • Kenneth Q. Zhou
  • Johnny Siu-Hang Li

Abstract

Recently, it has been argued that capital markets may share some of the overwhelming longevity risk exposures borne by the pension and life insurance industries. The transfer of risk can be accomplished by trading standardized derivatives such as q-forwards that are linked to published mortality indexes. To strategize such trades, one may utilize longevity Greeks, which are analogous to equity Greeks that have been used extensively in managing stock price risk. In this article, we first derive three important longevity Greeks—delta, gamma, and vega—on the basis of an extended version of the Lee-Carter model that incorporates stochastic volatility. We then study the properties of each longevity Greek and estimate the levels of effectiveness that different longevity Greek hedges can possibly achieve. The results reveal several interesting facts; for example, in a delta–vega hedge formed by q-forwards, the choice of reference ages does not materially affect hedge effectiveness, but the choice of times to maturity does. These facts may aid insurers to better formulate their hedge portfolios and issuers of mortality-linked securities to determine what security structures are more likely to attract liquidity.

Suggested Citation

  • Kenneth Q. Zhou & Johnny Siu-Hang Li, 2021. "Longevity Greeks: What Do Insurers and Capital Market Investors Need to Know?," North American Actuarial Journal, Taylor & Francis Journals, vol. 25(S1), pages 66-96, February.
  • Handle: RePEc:taf:uaajxx:v:25:y:2021:i:s1:p:s66-s96
    DOI: 10.1080/10920277.2019.1650283
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    Cited by:

    1. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    2. Zhou, Hongjuan & Zhou, Kenneth Q. & Li, Xianping, 2022. "Stochastic mortality dynamics driven by mixed fractional Brownian motion," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 218-238.
    3. Kenneth Q. Zhou & Johnny S.-H. Li & Pintao Lyu, 2024. "Bringing parametric mortality indexes to practice: a generalized CBD model with stochastic socioeconomic differentials in mortality improvements," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 295-319, April.
    4. Yang Qiao & Chou-Wen Wang & Wenjun Zhu, 2024. "Machine learning in long-term mortality forecasting," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 340-362, April.

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