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Advancements in Actuarial Mathematics and Insurance Risk Management
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Advancements in Actuarial Mathematics and Insurance Risk Management

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 10748

Special Issue Editor

Prof. Dr. Annamaria Olivieri

E-Mail Website
Guest Editor
Department is Economics and Management, University of Parma, Via Kennedy 6, 43100 Parma, Italy
Interests: risk management for life insurance and pension funds, in particular with reference to longevity risk; solvency for life portfolios and pension funds; actuarial perspectives of annuitization and post-retirement choices in pension products; multistate models for the insurances of the person; actuarial pricing of life and health insurance products; actuarial models for the valuation of the life insurance business
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The insurance industry is subject to a number of challenges: risks originated by evolving mortality, interest rates, inflation, climate changes; new tasks and constraints imposed by solvency regulation, accounting standards, and legislation; evolving preferences of individual and policyholder expectations; and risk–return targets of investors in dynamic scenarios.

Actuarial mathematics, within which we find the first example of a quantitative formalization of economic activity, can provide substantial support to face such challenges, but appropriate models need to be developed or revised.

The purpose of this Special Issue is to collect contributions in this respect. Topics of interest include, but are not limited to, the following:

  • Longevity risk and mortality modeling;
  • New products in life insurance, providing protection, investment opportunities or post-retirement income;
  • Innovative risk management solutions for insured risks;
  • Assessments of insurance liabilities along new reporting rules;
  • Retention vs. insurance in personal risk management;
  • Key indicators, summarizing an organization’s risk profile and performance.

Prof. Dr. Annamaria Olivieri
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

  • longevity risk
  • mortality modeling
  • new life insurance products
  • annuity design
  • insurance risk management
  • new reporting standards
  • key risk and performance indicators

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

17 pages, 1070 KiB  
Article
Can Multi-Peril Insurance Policies Mitigate Adverse Selection?
by Peter Zweifel and Annette Hofmann
Risks 2024, 12(6), 102; https://doi.org/10.3390/risks12060102 - 20 Jun 2024
Viewed by 1042
Abstract
The objective of this paper is to pursue an intuitive idea: for a consumer who represents an “unfavorable” health risk but an “excellent risk” as a driver, a multi-peril policy could be associated with a reduced selection effort on the part of the [...] Read more.
The objective of this paper is to pursue an intuitive idea: for a consumer who represents an “unfavorable” health risk but an “excellent risk” as a driver, a multi-peril policy could be associated with a reduced selection effort on the part of the insurer. If this intuition should be confirmed, it will serve to address the decade-long concern with risk selection both in the economic literature and on the part of policy makers. As an illustrative example, a two-peril model is developed in which consumers deploy effort in search of a policy offering them maximum coverage at the current market price while insurers deploy effort designed to stave off unfavorable risks. Two types of Nash equilibria are compared: one in which the insurer is confronted with high-risk and low-risk types, and another one where both types are a “better risk” with regard to a second peril. The difference in the insurer’s selection effort directed at high-risk and low-risk types is indeed shown to be lower in the latter case, resulting in a mitigation of adverse selection. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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23 pages, 8949 KiB  
Article
Climate Change-Related Disaster Risk Mitigation through Innovative Insurance Mechanism: A System Dynamics Model Application for a Case Study in Latvia
by Maksims Feofilovs, Andrea Jonathan Pagano, Emanuele Vannucci, Marina Spiotta and Francesco Romagnoli
Risks 2024, 12(3), 43; https://doi.org/10.3390/risks12030043 - 28 Feb 2024
Viewed by 2717
Abstract
This study explores how the System Dynamics modeling approach can help deal with the problem of conventional insurance mechanisms by studying the feedback loops governing complex systems connected to the disaster insurance mechanism. Instead of addressing the disaster’s underlying risk, the traditional disaster [...] Read more.
This study explores how the System Dynamics modeling approach can help deal with the problem of conventional insurance mechanisms by studying the feedback loops governing complex systems connected to the disaster insurance mechanism. Instead of addressing the disaster’s underlying risk, the traditional disaster insurance strategy largely focuses on providing financial security for asset recovery after a disaster. This constraint becomes especially concerning as the threat of climate-related disasters grows since it may result in rising long-term damage expenditures. A new insurance mechanism is suggested as a solution to this problem to lower damage costs while safeguarding insured assets and luring new assets to be protected. A local case study utilizing a System Dynamics stock and flow model is created and validated by examining the model’s structure, sensitivity analysis, and extreme value test. The results of the case study performed on a city in Latvia highlight the significance of effective disaster risk reduction strategies applied within the innovative insurance mechanism in lowering overall disaster costs. The logical coherence seen throughout the analysis of simulated scenario results strengthens the established model’s plausibility. The case study’s findings support the innovative insurance mechanism’s dynamic hypothesis and show the main influencing factors on the dynamics within the proposed innovative insurance mechanism. The information this study can help insurance firms, policy planners, and disaster risk managers make decisions that will benefit local communities and other stakeholders regarding climate-related disaster risk mitigation. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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27 pages, 510 KiB  
Article
Consumer Preferences for Health Services Offered by Health Insurance Companies in Germany
by Raphael Schilling, Milena Pavlova and Andrea Karaman
Risks 2023, 11(12), 216; https://doi.org/10.3390/risks11120216 - 12 Dec 2023
Viewed by 2812
Abstract
German health insurance companies increasingly strive to position themselves as health partners to their customers to improve customers’ health and contain costs. However, there is uncertainty about customers’ preferences for health services offered by health insurance companies. Therefore, this paper studies consumer preferences [...] Read more.
German health insurance companies increasingly strive to position themselves as health partners to their customers to improve customers’ health and contain costs. However, there is uncertainty about customers’ preferences for health services offered by health insurance companies. Therefore, this paper studies consumer preferences for health services that are or could be provided by health insurance companies in Germany. An online survey was conducted using two stated preference techniques to collect and analyze the data (namely, rating and ranking of health services considered by insurance companies). A sample of 880 German health insurance customers between 18 and 65 years old filled out the online questionnaire, of which 860 submitted complete responses. Ordinal logistic regression analysis was used for the rating and ranking. Preliminary examinations, care management, and health programs were the three health services most important to the respondents. The results suggest that people want their health insurance to support them with preventive health services that offer direct therapeutic value and not just informational, economic, access-related, or convenience-related benefits. These preferences for health services are homogeneous for most subgroups of the population, implying that health insurance companies could consider an overall strategy to address these preferences for all clients by focusing on the important health services. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
21 pages, 1107 KiB  
Article
New Classes of Distortion Risk Measures and Their Estimation
by Jungsywan H. Sepanski and Xiwen Wang
Risks 2023, 11(11), 194; https://doi.org/10.3390/risks11110194 - 10 Nov 2023
Cited by 2 | Viewed by 1936
Abstract
In this paper, we present a new method to construct new classes of distortion functions. A distortion function maps the unit interval to the unit interval and has the characteristics of a cumulative distribution function. The method is based on the transformation of [...] Read more.
In this paper, we present a new method to construct new classes of distortion functions. A distortion function maps the unit interval to the unit interval and has the characteristics of a cumulative distribution function. The method is based on the transformation of an existing non-negative random variable whose distribution function, named the generating distribution, may contain more than one parameter. The coherency of the resulting risk measures is ensured by restricting the parameter space on which the distortion function is concave. We studied cases when the generating distributions are exponentiated exponential and Gompertz distributions. Closed-form expressions for risk measures were derived for uniform, exponential, and Lomax losses. Numerical and graphical results are presented to examine the effects of the parameter values on the risk measures. We then propose a simple plug-in estimate of risk measures and conduct simulation studies to compare and demonstrate the performance of the proposed estimates. The plug-in estimates appear to perform slightly better than the well-known L-estimates, but also suffer from biases when applied to heavy-tailed losses. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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32 pages, 714 KiB  
Article
A Three-Factor Market Model for Incorporating Explicit General Inflation in Non-Life Claims Reserving
by Franco Moriconi
Risks 2023, 11(10), 174; https://doi.org/10.3390/risks11100174 - 7 Oct 2023
Cited by 1 | Viewed by 1598
Abstract
In a recent paper “Stochastic Chain-Ladder Reserving with Modeled General Inflation”, the effects of modeled general inflation on non-life claims reserving were studied using, along with the so called “market approach”, a stochastic two-factor market model, characterized by deterministic expected inflation. In the [...] Read more.
In a recent paper “Stochastic Chain-Ladder Reserving with Modeled General Inflation”, the effects of modeled general inflation on non-life claims reserving were studied using, along with the so called “market approach”, a stochastic two-factor market model, characterized by deterministic expected inflation. In the present paper, we repeat the same study, again with the market approach, using a three-factor market model which extends the two-factor model by including stochastic expected inflation. After detailing the theoretical model and estimating the relevant parameters on the same market data used in “Stochastic Chain-Ladder Reserving with Modeled General Inflation”, we repeat the application to claims reserving presented in that paper and compare the results obtained with the two models. With these data, it is found that the inclusion of stochastic expected inflation produces a non-negligible increase in the reserve solvency capital requirement under the one-year view. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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