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Risks
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Extreme Events: Mortality Modelling and Insurance
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Extreme Events: Mortality Modelling and Insurance

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 3600

Special Issue Editor

Dr. Sule Sahin

E-Mail Website
Guest Editor
Actuarial Science, Department of Accounting and Finance, School for Business and Society, University of York, Heslington, York YO10 5GD, UK
Interests: actuarial mathematics; economic scenario generators; mortality modelling; actuarial compensation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world population has experienced a steady increase in life expectancy through the twentieth century. However, population events such as extreme weather, pandemics, natural disasters, and wars cause jumps that have an immediate impact on mortality rates. Recent experiences such as the COVID-19 pandemic and heatwaves have proved that these extreme events can produce heterogeneous effects on populations, considering different age groups, sexes, ethnic and socio-economic backgrounds, etc. There has been an increase in the frequency, magnitude, and duration of natural disasters over the last 50 years, and rising global temperatures have particularly caused many diseases and deaths. The occurrence of catastrophic/extreme events, the large number of deaths, as well as unexpected death claims have financial consequences for insurance companies. In fact, these catastrophic events increase the credit risk and threaten the solvency of insurance companies, and even countries. The financial impacts of these risks on solvency require effective risk management.

This Special Issue welcomes state-of-the-art research papers related but not limited to mortality/excess mortality modelling and risk management in insurance, including the effects of extreme events on mortality, valuation of insurance and annuity products, hedging mortality/longevity risks, pricing of mortality-linked and longevity-linked securities, solvency, credit risk, counterparty default risk, financial liquidity, derivatives and asset management.

Dr. Sule Sahin
Guest Editor

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.

Keywords

  • mortality/excess-mortality modelling
  • extreme events and insurance risk
  • mortality/longevity risk
  • hedging
  • credit risk
  • financial risk management
  • COVID-19
  • pandemics

Published Papers (3 papers)

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Research

27 pages, 685 KiB  
Article
COVID-19 and Excess Mortality: An Actuarial Study
by Camille Delbrouck and Jennifer Alonso-García
Risks 2024, 12(4), 61; https://doi.org/10.3390/risks12040061 - 30 Mar 2024
Viewed by 589
Abstract
The study of mortality is an ever-active field of research, and new methods or combinations of methods are constantly being developed. In the actuarial domain, the study of phenomena disrupting mortality and leading to excess mortality, as in the case of COVID-19, is [...] Read more.
The study of mortality is an ever-active field of research, and new methods or combinations of methods are constantly being developed. In the actuarial domain, the study of phenomena disrupting mortality and leading to excess mortality, as in the case of COVID-19, is of great interest. Therefore, it is relevant to investigate the extent to which an epidemiological model can be integrated into an actuarial approach in the context of mortality. The aim of this project is to establish a method for the study of excess mortality due to an epidemic and to quantify these effects in the context of the insurance world to anticipate certain possible financial instabilities. We consider a case study caused by SARS-CoV-2 in Belgium during the year 2020. We propose an approach that develops an epidemiological model simulating excess mortality, and we incorporate this model into a classical approach to pricing life insurance products. Full article
(This article belongs to the Special Issue Extreme Events: Mortality Modelling and Insurance)
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24 pages, 1235 KiB  
Article
A Quantitative Comparison of Mortality Models with Jumps: Pre- and Post-COVID Insights on Insurance Pricing
by Şule Şahin and Selin Özen
Risks 2024, 12(3), 53; https://doi.org/10.3390/risks12030053 - 14 Mar 2024
Viewed by 794
Abstract
Population events such as natural disasters, pandemics, extreme weather, and wars might cause jumps that have an immediate impact on mortality rates. The recent COVID-19 pandemic has demonstrated that these events should not be treated as nonrepetitive exogenous interventions. Therefore, mortality models incorporating [...] Read more.
Population events such as natural disasters, pandemics, extreme weather, and wars might cause jumps that have an immediate impact on mortality rates. The recent COVID-19 pandemic has demonstrated that these events should not be treated as nonrepetitive exogenous interventions. Therefore, mortality models incorporating jump effects are particularly important to capture the adverse mortality shocks. The mortality models with jumps, which we consider in this study, differ in terms of the duration of the jumps–transitory or permanent–the frequency of the jumps, and the size of the jumps. To illustrate the effect of the jumps, we also consider benchmark mortality models without jump effects, such as the Lee-Carter model, Renshaw and Haberman model and Cairns-Blake-Dowd model. We discuss the performance of all the models by analysing their ability to capture the mortality deterioration caused by COVID-19. We use data from different countries to simulate the mortality rates for the pandemic and post-pandemic years and examine their accuracy in forecasting the mortality jumps due to the pandemic. Moreover, we also examine the jump-free and jump models in terms of their impact on insurance pricing, specifically term annuity and life insurance present values calibrated for both pre- and post-COVID data. Full article
(This article belongs to the Special Issue Extreme Events: Mortality Modelling and Insurance)
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24 pages, 1988 KiB  
Article
LSTM-Based Coherent Mortality Forecasting for Developing Countries
by Jose Garrido, Yuxiang Shang and Ran Xu
Risks 2024, 12(2), 27; https://doi.org/10.3390/risks12020027 - 01 Feb 2024
Viewed by 1251
Abstract
This paper studies a long short-term memory (LSTM)-based coherent mortality forecasting method for developing countries or regions. Many of such developing countries have experienced a rapid mortality decline over the past few decades. However, their recent mortality development trend is not necessarily driven [...] Read more.
This paper studies a long short-term memory (LSTM)-based coherent mortality forecasting method for developing countries or regions. Many of such developing countries have experienced a rapid mortality decline over the past few decades. However, their recent mortality development trend is not necessarily driven by the same factors as their long-term behavior. Hence, we propose a time-varying mortality forecasting model based on the life expectancy and lifespan disparity gap between these developing countries and a selected benchmark group. Here, the mortality improvement trend for developing countries is expected to converge gradually to that of the benchmark group during the projection phase. More specifically, we use a unified deep neural network model with LSTM architecture to project the life expectancy and lifespan disparity difference, which further controls the rotation of the time-varying weight parameters in the model. This approach is applied to three developing countries and three developing regions. The empirical results show that this LSTM-based coherent forecasting method outperforms classical methods, especially for the long-term projections of mortality rates in developing countries. Full article
(This article belongs to the Special Issue Extreme Events: Mortality Modelling and Insurance)
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