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CBDX: a workhorse mortality model from the Cairns–Blake–Dowd family

Published online by Cambridge University Press:  22 June 2020

Kevin Dowd*
Affiliation:
Durham University Business School, Mill Hill Lane, DurhamDHL 3LB, United Kingdom
Andrew J. G. Cairns
Affiliation:
Maxwell Institute for Mathematical Sciences and Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom
David Blake
Affiliation:
Pensions Institute, Cass Business School, City University of London, 106 Bunhill Row, London,EC1Y 8TZ, United Kingdom.
*
*Corresponding author. E-mail: [email protected].

Abstract

The purpose of this paper is to identify a workhorse mortality model for the adult age range (i.e., excluding the accident hump and younger ages). It applies the “general procedure” (GP) of Hunt & Blake [(2014), North American Actuarial Journal, 18, 116–138] to identify an age-period model that fits the data well before adding in a cohort effect that captures the residual year-of-birth effects arising in the original age-period model. The resulting model is intended to be suitable for a variety of populations, but economises on the number of period effects in comparison with a full implementation of the GP. We estimate the model using two different iterative maximum likelihood (ML) approaches – one Partial ML and the other Full ML – that avoid the need to specify identifiability constraints.

Type
Paper
Copyright
© Institute and Faculty of Actuaries 2020

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References

Cairns, A.J.G. (2014). Modeling and management of longevity risk. In P.B. Hammond, R. Maurer & O.S. Mitchell (Eds.), Recreating Sustainable Retirement: Resilience, Solvency, and Tail Risk (pp. 7188) Oxford, UK: Oxford University Press.CrossRefGoogle Scholar
Cairns, A.J.G, Blake, D. & Dowd, K. (2006) A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. Journal of Risk and Insurance, 73, 687718.CrossRefGoogle Scholar
Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A. & Balevich, I. (2009) A quantitative comparison of stochastic mortality models using data from England and Wales and the United States. North American Actuarial Journal, 13(1), 135.CrossRefGoogle Scholar
Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G.D. & Khalaf-Allah, M. (2011a). Bayesian stochastic mortality modelling for two populations. ASTIN Bulletin 41(1), 2959.Google Scholar
Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G.D. & Khalaf-Allah, M. (2011b). Mortality mortality density forecasts: an analysis of six stochastic mortality models. Insurance: Mathematics and Economics, 48, 355367.Google Scholar
Cairns, A.J.G., Blake, D., Dowd, K. & Kessler, A.R. (2016). Phantoms never die: living with unreliable population data. Journal of the Royal Statistical Society A, 179, Part 4, 9751005.CrossRefGoogle Scholar
CMI (2018). CMI Mortality Projections Model: CMI_2017. CMI Working Paper 105, Institute and Faculty of Actuaries.Google Scholar
Dowd, K., Blake, D. & Cairns, A.J.G. (2018). Hedging annuity risks with the age-period-cohort two-population gravity model. North American Actuarial Journal. https://www.tandfonline.com/doi/full/10.1080/10920277.2019.1652102.Google Scholar
Dowd, K., Cairns, A.J.G., Blake, D., Coughlan, G.D., Epstein, D. & Khalaf-Allah, M. (2010). Backtesting stochastic mortality models: an ex-post evaluation of multi-period-ahead density forecasts. North American Actuarial Journal, 14(4), 281298.CrossRefGoogle Scholar
Dowd, K., Cairns, A.J.G., Blake, D., Coughlan, G.D., Epstein, D. & Khalaf-Allah, M. (2011). A gravity model of mortality rates for two related population. North American Actuarial Journal, 15(2), 334356.CrossRefGoogle Scholar
Hunt, A. & Blake, D. (2014). A general procedure for constructing mortality models. North American Actuarial Journal, 18, 116138.CrossRefGoogle Scholar
Hunt, A. & Blake, D. (2020a). Identifiability in Age/Period Mortality Models. Annals of Actuarial Science.CrossRefGoogle Scholar
Hunt, A. & Blake, D. (2020b). Identifiability in Age/Period/Cohort Mortality Models. Annals of Actuarial Science.CrossRefGoogle Scholar
Lee, R.D. & Carter, L.R. (1992). Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659675.Google Scholar
Plat, R. (2009). On stochastic mortality modeling. Insurance: Mathematics and Economics, 45, 393404.Google Scholar
PRA. (2016). Solvency II: consolidation of Directors’ letters. Consultation Paper CP20/16, Prudential Regulation Authority, Bank of England.Google Scholar
Renshaw, A.E. & Haberman, S. (2003). Lee-Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33, 255272.Google Scholar
Renshaw, A.E. & Haberman, S. (2006). A Cohort-based extension to the Lee Carter model for mortality reduction factors. Insurance: Mathematics and Economics, 38, 556570.Google Scholar