The University of Southampton
University of Southampton Institutional Repository

Bayesian mortality forecasting with overdispersion

Bayesian mortality forecasting with overdispersion
Bayesian mortality forecasting with overdispersion
The ability to produce accurate mortality forecasts, accompanied by a set of representative uncertainty bands, is crucial in the planning of public retirement funds and various life-related businesses. In this paper, we focus on one of the drawbacks of the Poisson Lee–Carter model (Brouhns et al., 2002) that imposes mean–variance equality, restricting mortality variations across individuals. Specifically, we present two models to potentially account for overdispersion. We propose to fit these models within the Bayesian framework for various advantages, but primarily for coherency. Markov Chain Monte Carlo (MCMC) methods are implemented to carry out parameter estimation. Several comparisons are made with the Bayesian Poisson Lee–Carter model (Czado et al., 2005) to highlight the importance of accounting for overdispersion. We demonstrate that the methodology we developed prevents over-fitting and yields better calibrated prediction intervals for the purpose of mortality projections. Bridge sampling is used to approximate the marginal likelihood of each candidate model to compare the models quantitatively.
Mortality forecast, overdispersion, Bayesian methods , MCMC, Bridge Sampling
0167-6687
Wong, Jackie S.T.
bc6647ec-62bc-4b36-b883-2afe7c529f65
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Wong, Jackie S.T.
bc6647ec-62bc-4b36-b883-2afe7c529f65
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940

Wong, Jackie S.T., Forster, Jonathan J. and Smith, Peter W.F. (2017) Bayesian mortality forecasting with overdispersion. Insurance: Mathematics and Economics.

Record type: Article

Abstract

The ability to produce accurate mortality forecasts, accompanied by a set of representative uncertainty bands, is crucial in the planning of public retirement funds and various life-related businesses. In this paper, we focus on one of the drawbacks of the Poisson Lee–Carter model (Brouhns et al., 2002) that imposes mean–variance equality, restricting mortality variations across individuals. Specifically, we present two models to potentially account for overdispersion. We propose to fit these models within the Bayesian framework for various advantages, but primarily for coherency. Markov Chain Monte Carlo (MCMC) methods are implemented to carry out parameter estimation. Several comparisons are made with the Bayesian Poisson Lee–Carter model (Czado et al., 2005) to highlight the importance of accounting for overdispersion. We demonstrate that the methodology we developed prevents over-fitting and yields better calibrated prediction intervals for the purpose of mortality projections. Bridge sampling is used to approximate the marginal likelihood of each candidate model to compare the models quantitatively.

Text
paper_IME_main - Accepted Manuscript
Download (1MB)

More information

Accepted/In Press date: 28 September 2017
e-pub ahead of print date: 19 October 2017
Keywords: Mortality forecast, overdispersion, Bayesian methods , MCMC, Bridge Sampling

Identifiers

Local EPrints ID: 416735
URI: http://eprints.soton.ac.uk/id/eprint/416735
ISSN: 0167-6687
PURE UUID: c6ecc7ab-65d2-4925-9de6-9c19cf26acb6
ORCID for Jonathan J. Forster: ORCID iD orcid.org/0000-0002-7867-3411
ORCID for Peter W.F. Smith: ORCID iD orcid.org/0000-0003-4423-5410

Catalogue record

Date deposited: 05 Jan 2018 17:30
Last modified: 17 Dec 2019 05:45

Export record

Contributors

Author: Jackie S.T. Wong
Author: Jonathan J. Forster ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×