Longevity Risk, Insurance and Pensions

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 30 May 2024 | Viewed by 4083

Special Issue Editors


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Guest Editor
Associate Professor of Mathematical Methods of Economics, Finance and Actuarial Sciences, Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, I-84084 Fisciano, SA, Italy
Interests: longevity risk; mortality projections; uncertainty; retirement scheme

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Guest Editor
Faculty of Actuarial Science and Insurance, Bayes Business School, City University of London, 106 Bunhill Row, London EC1Y 8TZ, UK
Interests: actuarial science; applied statistics; insurance; modelling of mortality trends and longevity risk; pensions and annuity modelling

Special Issue Information

Dear Colleagues,

The progressive increase in life expectancy at advanced ages profoundly affects developed countries, representing a central issue for governments and insurance companies in many different fields. The longevity risk, originating from the uncertainty in the evolution of mortality at adult ages, represents the risk of underestimating the average expected longevity. This causes significant financial pressure on public and private insurance and pension sectors, representing a serious threat to both public finances and life insurance companies.

This Special Issue aims to collect recent results in the research area of longevity risk and its implications for insurance companies, as well as for public and private pension systems. We invite papers presenting original research on related topics including, but not limited to, the following:

  1. Mortality improvements/longevity;
  2. Modeling and projection techniques for mortality rates and mortality improvement rates;
  3. Implications and changes needed in the public and private sector to support the income needs of a growing aging population;
  4. Public policy issues and potential solutions;
  5. New opportunities for retirement systems and insurance products to meet the needs of an increasingly aged population.

Dr. Maria Russolillo
Prof. Dr. Steven Haberman
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

21 pages, 694 KiB  
Article
Should Selection of the Optimum Stochastic Mortality Model Be Based on the Original or the Logarithmic Scale of the Mortality Rate?
by Miguel Santolino
Risks 2023, 11(10), 170; https://doi.org/10.3390/risks11100170 - 28 Sep 2023
Viewed by 978
Abstract
Stochastic mortality models seek to forecast future mortality rates; thus, it is apparent that the objective variable should be the mortality rate expressed in the original scale. However, the performance of stochastic mortality models—in terms, that is, of their goodness-of-fit and prediction accuracy—is [...] Read more.
Stochastic mortality models seek to forecast future mortality rates; thus, it is apparent that the objective variable should be the mortality rate expressed in the original scale. However, the performance of stochastic mortality models—in terms, that is, of their goodness-of-fit and prediction accuracy—is often based on the logarithmic scale of the mortality rate. In this article, we examine whether the same forecast outcomes are obtained when the performance of mortality models is assessed based on the original and log scales of the mortality rate. We compare four different stochastic mortality models: the original Lee–Carter model, the Lee–Carter model with (log)normal distribution, the Lee–Carter model with Poisson distribution and the median Lee–Carter model. We show that the preferred model will depend on the scale of the objective variable, the selection criteria measure and the range of ages analysed. Full article
(This article belongs to the Special Issue Longevity Risk, Insurance and Pensions)
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15 pages, 867 KiB  
Article
Gender Pension Gap in EU Countries: A Between-Group Inequality Approach
by Antonio Abatemarco, Elena Lagomarsino and Maria Russolillo
Risks 2023, 11(3), 63; https://doi.org/10.3390/risks11030063 - 20 Mar 2023
Cited by 1 | Viewed by 2363
Abstract
Pension entitlements are influenced by individual career paths and labor market conditions, which often result in gender-based disparities. Women face several challenges during their working lives, such as late entry into the labor market, the gender pay gap, discontinuous working careers, and early [...] Read more.
Pension entitlements are influenced by individual career paths and labor market conditions, which often result in gender-based disparities. Women face several challenges during their working lives, such as late entry into the labor market, the gender pay gap, discontinuous working careers, and early retirement due to family caregiving, which lead to lower pension incomes. This paper investigates the gender pension gap in nine European Union countries from 2004 to 2020. Our study adopts a non-parametric estimation strategy that utilizes additively decomposable inequality measures to provide a more informative perspective on gender inequality. We aim to demonstrate that this approach surpasses the standard gender gap in pension index in capturing between-gender inequality in societies. Employing data from the SHARE database, we find that gender inequality in the studied countries is decreasing on average, with a convergence trend observed from 2011 onwards. This study contributes to a more comprehensive understanding of the gender pension gap phenomenon, which is crucial for developing effective policy responses in a welfare perspective. Full article
(This article belongs to the Special Issue Longevity Risk, Insurance and Pensions)
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