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IFRS Convergence: The Role of Stochastic Mortality Models in the Disclosure of Longevity Risk for Defined Benefit Plans

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  • Fujisawa Yosuke

    (University of Waterloo)

  • Li Johnny Siu-Hang

    (University of Waterloo)

Abstract

In recent years, the International Accounting Standards Board (IASB) and its International Financial Reporting Standards (IFRSs) have made great strides toward achieving global accounting convergence. Various countries, including Japan and Canada, are either adopting or converging their national standards with IFRSs. The IASB is now undertaking a comprehensive review of the accounting standards on post-employment benefits, an important part of which is about quantitative disclosures of longevity risk. In this paper we examine how stochastic mortality models may assist with such disclosures. Specifically, we present three concepts that can help defined benefit plans identify the materiality of their longevity risk exposures: (1) longevity value-at-risk, (2) probability of longevity deficit, and (3) the probabilistic corridor rule. We illustrate these concepts with a hypothetical pension plan in Japan.

Suggested Citation

  • Fujisawa Yosuke & Li Johnny Siu-Hang, 2011. "IFRS Convergence: The Role of Stochastic Mortality Models in the Disclosure of Longevity Risk for Defined Benefit Plans," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 5(1), pages 1-27, March.
  • Handle: RePEc:bpj:apjrin:v:5:y:2011:i:1:n:2
    DOI: 10.2202/2153-3792.1077
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    References listed on IDEAS

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    1. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    2. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    3. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    4. Blake, David & Dowd, Kevin & Cairns, Andrew J.G., 2008. "Longevity risk and the Grim Reaper's toxic tail: The survivor fan charts," Insurance: Mathematics and Economics, Elsevier, vol. 42(3), pages 1062-1066, June.
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