A Bayesian non-parametric model for small population mortality
Hong Li () and
Yang Lu
Post-Print from HAL
Abstract:
This paper proposes a Bayesian non-parametric mortality model for a small population, when a benchmark mortality table is also available and serves as part of the prior information. In particular, we extend the Poisson-gamma model of Hardy and Panjer to incorporate correlated and age-specific mortality coefficients. These coefficients, which measure the difference in mortality levels between the small and the benchmark population, follow an age-indexed autoregressive gamma process, and can be stochastically extrapolated to ages where the small population has no historical exposure. Our model substantially improves the computation efficiency of existing two-population Bayesian mortality models by allowing for closed form posterior mean and variance of the future number of deaths, and an efficient sampling algorithm for the entire posterior distribution. We illustrate the proposed model with a life insurance portfolio from a French insurance company.
Keywords: credibility; Autoregressive gamma; two-population mortality model; parameter uncertainty (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-age
Note: View the original document on HAL open archive server: https://hal.science/hal-02419000v1
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)
Published in Scandinavian Actuarial Journal, 2018, 2018 (7), pp.605-628. ⟨10.1080/03461238.2017.1418420⟩
Downloads: (external link)
https://hal.science/hal-02419000v1/document (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02419000
DOI: 10.1080/03461238.2017.1418420
Access Statistics for this paper
More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().