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Risks
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Longevity/Mortality Risk Measurement and Management in Actuarial Sciences
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Longevity/Mortality Risk Measurement and Management in Actuarial Sciences

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 7439

Special Issue Editors

Prof. Dr. Rui Zhou

E-Mail Website
Guest Editor
Department of Economics, University of Melbourne, Parkville, VIC 3010, Australia
Interests: longevity/mortality risk measurement and management; mortality modeling and forecasting; longevity annuity; weather derivatives
Dr. Yanxin Liu

E-Mail Website
Guest Editor
Actuarial Science, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Interests: actuarial science; longevity risk; mortality risk
Dr. Kenneth Zhou

E-Mail Website
Guest Editor
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA
Interests: actuarial science; risk management

Special Issue Information

Dear Colleagues,

Uncertainty in future mortality imposes significant financial strain on the insurance and pension sector. Longevity risk measurement and management have received increasing interest from annuity providers and pension sponsors, who suffer under stronger-than-expected mortality improvements. The recent COVID-19 outbreak, which impacts both short-term and long-term mortality, presents new challenges to insurers in mortality forecasting and mortality risk management.

This Special Issue of Risks is devoted to the measurement and management of longevity and mortality risks. We invite papers presenting original research on related topics, including, but not limited to, the following:

  • Mortality shocks due to pandemics and natural disasters;
  • Stochastic mortality models;
  • Spatio-temporal mortality dependences;
  • Multi-population mortality models;
  • Longevity risk measurement and management;
  • Application of machine learning techniques in mortality- and longevity-related research;
  • Securitization of longevity/mortality risks;
  • Retirement product innovation, such as reverse mortgage, pooled annuity, and index-linked annuity;
  • Mortality modelling and forecasting by cause of death;
  • Educational, socioeconomic, racial, and ethnic differentials in longevity/mortality.

Prof. Dr. Rui Zhou
Dr. Yanxin Liu
Dr. Kenneth Zhou
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 (3 papers)

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Research

18 pages, 462 KiB  
Article
Disentangling Trend Risk and Basis Risk with Functional Time Series
by Yanxin Liu and Johnny Siu-Hang Li
Risks 2023, 11(12), 208; https://doi.org/10.3390/risks11120208 - 28 Nov 2023
Viewed by 1220
Abstract
In recent multi-population stochastic mortality models, one critical scientific issue is the vague distinction between trend risk and population basis risk. In particular, the cross- and auto-correlations between the innovations of the latent factors representing the common trend and the population-specific trends are [...] Read more.
In recent multi-population stochastic mortality models, one critical scientific issue is the vague distinction between trend risk and population basis risk. In particular, the cross- and auto-correlations between the innovations of the latent factors representing the common trend and the population-specific trends are often assumed to be non-existent, although they are possibly statistically significant. While it is theoretically possible to capture such correlations by treating the latent factors as a vector time series, the resulting model would contain a large number of parameters, which may in turn lead to robustness problems. In this paper, we address these issues by the use of the product–ratio model. Contrary to the prevalent assumption of non-existent correlations, the latent factors under the product–ratio model are approximately uncorrelated. This permits us to disentangle trend risk and population basis risk, thereby sparing us from the need to use a heavily parameterized vector time-series process. Compared to the augmented common factor model, our approach demonstrates improved robustness in terms of correlation structures and hedging performance, offering a new perspective on treating cross- and auto-correlations between latent factors in mortality modeling. Full article
(This article belongs to the Special Issue Longevity/Mortality Risk Measurement and Management in Actuarial Sciences)
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21 pages, 39065 KiB  
Article
Air Pollution and Mortality Impacts
by Zhe Michelle Dong, Han Lin Shang and Aaron Bruhn
Risks 2022, 10(6), 126; https://doi.org/10.3390/risks10060126 - 14 Jun 2022
Cited by 3 | Viewed by 2278
Abstract
This study quantifies the air quality impact on population mortality from an actuarial perspective, considering implications to the industry through the application of findings. The study focuses on the increase in mortality from air quality changes due to extreme weather impacts. We conduct [...] Read more.
This study quantifies the air quality impact on population mortality from an actuarial perspective, considering implications to the industry through the application of findings. The study focuses on the increase in mortality from air quality changes due to extreme weather impacts. We conduct an empirical study using monthly Californian climate and mortality data from 1999 to 2019 to determine whether adding PM2.5 as a factor improves forecast excess mortality. Expected mortality is defined using the rolling five-year average of observed mortality for each county. We compared three statistical models, namely a Generalised Linear Model (GLM), a Generalised Additive Model (GAM), and an Extreme Gradient Boosting (XGB) regression model. We find including PM2.5 improves the performance of all three models and that the GAM performs the best in terms of predictive accuracy. Change points are also considered to determine whether significant events trigger changes in mortality over extended periods. Based on several identified change points, some wildfires trigger heightened excess mortality. Full article
(This article belongs to the Special Issue Longevity/Mortality Risk Measurement and Management in Actuarial Sciences)
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34 pages, 8312 KiB  
Article
Temporal Clustering of the Causes of Death for Mortality Modelling
by Nicholas Bett, Juma Kasozi and Daniel Ruturwa
Risks 2022, 10(5), 99; https://doi.org/10.3390/risks10050099 - 6 May 2022
Cited by 3 | Viewed by 2520
Abstract
Actuaries utilize demographic features such as mortality and longevity rates for pricing, valuation, and reserving life insurance and pension contracts. Capturing accurate mortality estimates requires factual mortality assumptions in mortality models. However, the dynamic and uncertain nature of mortality improvements and deteriorations necessitates [...] Read more.
Actuaries utilize demographic features such as mortality and longevity rates for pricing, valuation, and reserving life insurance and pension contracts. Capturing accurate mortality estimates requires factual mortality assumptions in mortality models. However, the dynamic and uncertain nature of mortality improvements and deteriorations necessitates better approaches in tracking mortality changes, for instance, using the causes of deaths features. This paper aims to determine temporal homogeneous clusters using unsupervised learning, a clustering approach to group causes of death based on (dis)similarity measures to set representative clusters in detection and monitoring death trends. The causes of death dataset were derived from the World Health Organization, Global Health Estimates for males and females, from 2000 to 2019, for Kenya. A hierarchical agglomerative clustering technique was implemented with modified Dynamic Time Warping distance criteria. Between 6 and 14 clusters were optimally achieved for both males and females. Using visualisations, principal clusters were detected. Over time, the causes of death trends of these clusters have demonstrated a correlated association with mortality and longevity rates, rationalizing why insurance and pension offices may include this approach as a preliminary step to undertake mortality and longevity modelling. Full article
(This article belongs to the Special Issue Longevity/Mortality Risk Measurement and Management in Actuarial Sciences)
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