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Article

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

ISFA, LSAF EA2429, Univ Lyon, Université Claude Bernard Lyon 1, 50 Avenue Tony Garnier, F-69007 Lyon, France
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This work is mainly supported by the AXA research fund project on “Niveaux et tendances d’amélioration de la longévité: sélection de modèle pour les hypothèses “Best Estimate” et détection de rupture à l’aide de tests séquentiels, avec prise en compte du contexte covid-19”. S. Loisel and Y. Salhi also acknowledge support from the BNP Paribas Cardif Chair “Data Analytics & Models for Insurance” and the Milliman research initiative “Actuariat Durable”.
Received: 18 November 2020 / Revised: 8 December 2020 / Accepted: 16 December 2020 / Published: 24 December 2020
(This article belongs to the Special Issue Mortality Forecasting and Applications)
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. View Full-Text
Keywords: mortality; forecasting; regularization; elastic-net; smoothing; Poisson generalized linear model mortality; forecasting; regularization; elastic-net; smoothing; Poisson generalized linear model
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MDPI and ACS Style

Barigou, K.; Loisel, S.; Salhi, Y. Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect. Risks 2021, 9, 5. https://doi.org/10.3390/risks9010005

AMA Style

Barigou K, Loisel S, Salhi Y. Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect. Risks. 2021; 9(1):5. https://doi.org/10.3390/risks9010005

Chicago/Turabian Style

Barigou, Karim, Stéphane Loisel, and Yahia Salhi. 2021. "Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect" Risks 9, no. 1: 5. https://doi.org/10.3390/risks9010005

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