University of Lyon Researchers Update Current Study Findings on Risk Management (Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect): Insurance - Risk Management - Insurance News | InsuranceNewsNet

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December 3, 2021 Newswires
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University of Lyon Researchers Update Current Study Findings on Risk Management (Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect): Insurance – Risk Management

NewsRx COVID-19 Daily

2021 DEC 03 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx COVID-19 Daily -- Current study results on insurance have been published. According to news reporting out of Lyon, France, by NewsRx editors, research stated, “Predicting the evolution of mortality rates plays a central role for life insurance and pension funds.”

Financial supporters for this research include AXA research fund.

Our news reporters obtained a quote from the research from University of Lyon: “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.”

According to the news editors, the research concluded: “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.”

For more information on this research see: Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect. Risks, 2021,9(5):5. (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks9010005.

Our news journalists report that more information may be obtained by contacting Karim Barigou, ISFA, LSAF EA2429, University of Lyon, Universite Claude Bernard Lyon 1, 50 Avenue Tony Garnier, F-69007 Lyon, France. Additional authors for this research include Stephane Loisel, Yahia Salhi.

(Our reports deliver fact-based news of research and discoveries from around the world.)

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