Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect

Karim Barigou, Stéphane Loisel and Yahia Salhi
Additional contact information
Karim Barigou: ISFA, LSAF EA2429, Univ Lyon, Université Claude Bernard Lyon 1, 50 Avenue Tony Garnier, F-69007 Lyon, France
Yahia Salhi: ISFA, LSAF EA2429, Univ Lyon, Université Claude Bernard Lyon 1, 50 Avenue Tony Garnier, F-69007 Lyon, France

Risks, 2020, vol. 9, issue 1, 1-18

Abstract: Predicting the evolution of mortality rates plays a central role for life insurance and pension funds. Standard single population models typically suffer from two major drawbacks: on the one hand, they use a large number of parameters compared to the sample size and, on the other hand, model choice is still often based on in-sample criterion, such as the Bayes information criterion (BIC), and therefore not on the ability to predict. In this paper, we develop a model based on a decomposition of the mortality surface into a polynomial basis. Then, we show how regularization techniques and cross-validation can be used to obtain a parsimonious and coherent predictive model for mortality forecasting. We analyze how COVID-19-type effects can affect predictions in our approach and in the classical one. In particular, death rates forecasts tend to be more robust compared to models with a cohort effect, and the regularized model outperforms the so-called P-spline model in terms of prediction and stability.

Keywords: mortality; forecasting; regularization; elastic-net; smoothing; Poisson generalized linear model (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-9091/9/1/5/pdf (application/pdf)
https://www.mdpi.com/2227-9091/9/1/5/ (text/html)

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:gam:jrisks:v:9:y:2020:i:1:p:5-:d:467790

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().