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Risks
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Statistics and Quantitative Risk Management for Insurance
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Statistics and Quantitative Risk Management for Insurance

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 6509

Special Issue Editors

Dr. Georgios Pitselis

E-Mail Website
Guest Editor
Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece
Interests: credibility premium estimation; robust estimation; ratemaking and reserving; solvency ii; actuarial risk management; non-life risks; modelling mortality and longevity risk; econometric models for insurance
Special Issues, Collections and Topics in MDPI journals
Dr. Apostolos Bozikas

E-Mail Website
Guest Editor
Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece
Interests: credibility theory; stochastic mortality modelling; reserving; securitization of longevity risk; actuarial pension plans; actuarial risk management; life and health insurance pricing
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis Badounas

E-Mail Website
Guest Editor
Q Representative Insurance & Reinsurance Companies S.A., 17121 Athens, Greece
Interests: loss reserving; non-life insurance; robust estimation; ruin theory; longevity risk management

Special Issue Information

Dear Colleagues,

We are pleased to inform you that we are guest editing a Special Issue entitled “Statistics and Quantitative Risk Management for Insurance” which will be published in Risks (https://www.mdpi.com/journal/risks, ISSN 2227-9091). This Special Issue is now open to receive submissions of full research articles and comprehensive review papers for peer-review. Details can be found at the following link: https://www.mdpi.com/journal/risks/special_issues/statistics_quantitative_risk_management_for_insurance. For further details on the submission process, please see the instructions for authors at the journal website: https://www.mdpi.com/journal/risks/instructions.

Quantitative risk management (QRM) is one of the more challenging tasks for financial institutions, such as banks, insurance companies, etc. As financial products are becoming more complex, new risk management methods have to be adjusted to these new products. QRM incorporates a wide range of techniques from different disciplines (including statistics, mathematics, and finance) to address issues related to the operations and the regulations of a financial institution.

This Special Issue aims to highlight the interplay between the statistical theory and the risk management process. We thus welcome submissions of high-quality articles that present recent developments or introduce new theoretical (or practical) advances in the area of statistics and quantitative risk management with applications related to insurance industry.

Some examples of possible research topics for this Special Issue include among others:

  • catastrophe risk management
  • computational methods for insurance pricing
  • cyber insurance and risk management
  • econometric models for risk management
  • estimation and evaluation of risk management models
  • extreme value theory in risk management
  • insurance risk management
  • longevity / mortality modelling and risk management
  • loss distributions and their applications in insurance risk management
  • solvency for financial institutions and risk aggregation

Prof. Dr. Georgios Pitselis
Dr. Apostolos Bozikas
Dr. Ioannis Badounas
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.

Keywords

  • actuarial science
  • cyber risk management
  • dependence modelling
  • extreme events
  • finance
  • insurance
  • longevity risk
  • multivariate analysis
  • quantitative risk management
  • risk analysis
  • risk management
  • risk measures
  • statistical methods

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

30 pages, 774 KiB  
Article
Extensions on the Hatzopoulos–Sagianou Multiple-Components Stochastic Mortality Model
by Aliki Sagianou and Peter Hatzopoulos
Risks 2022, 10(7), 131; https://doi.org/10.3390/risks10070131 - 21 Jun 2022
Viewed by 1856
Abstract
In this paper, we present extensions of the Hatzopoulos–Sagianou (2020) (HS) multiple-component stochastic mortality model. Our aim is to thoroughly evaluate and stress test the HS model by deploying various link functions, using generalised linear models, and diverse distributions in the model’s estimation [...] Read more.
In this paper, we present extensions of the Hatzopoulos–Sagianou (2020) (HS) multiple-component stochastic mortality model. Our aim is to thoroughly evaluate and stress test the HS model by deploying various link functions, using generalised linear models, and diverse distributions in the model’s estimation method. In this work, we differentiate the HS approach by modelling the number of deaths using the Binomial model commonly employed in the literature of mortality modelling. Given this, new HS extensions are derived using the off-the-shelf link functions, namely the complementary log–log, logit and probit, while we also reform the model by introducing a new form of link functions with a particular focus on the use of heavy-tailed distributions. The above-mentioned enhancements conclude to a new methodology for the HS model, and we prove that it is more suitable than those used in the literature to model the mortality dynamics. In this regard, our work offers an extensive experimental testbed to scrutinise the efficiency, explainability and capacity of the HS model extensions using both the off-the-shelf and the newly introduced form of link functions over datasets with different characteristics. The introduced HS extensions bring an improvement by approximately 16% to the model’s goodness-of-fit, while they uncover more fine-grained age clusters. In addition, we compare the performance of the HS extensions against other well-known mortality models, both under fitting and forecast modes. The results reflect the advantageous features of the HS extensions to deliver a highly informative structure and enable the attribution of an identified mortality trend to a unique age cluster. The above-mentioned improvements enable mortality analysts to perform an in-depth and more detailed investigation of mortality trends for specific age clusters and can contribute to the attempts of academia and industry to tackle the uncertainties and risks introduced by the increasing life expectancy. Full article
(This article belongs to the Special Issue Statistics and Quantitative Risk Management for Insurance)
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15 pages, 924 KiB  
Article
Pricing Longevity Bonds under a Credibility Framework with Limited Available Data
by Apostolos Bozikas, Ioannis Badounas and Georgios Pitselis
Risks 2022, 10(5), 96; https://doi.org/10.3390/risks10050096 - 4 May 2022
Viewed by 2977
Abstract
For annuity providers, a higher life expectancy is not always positive news, as it potentially implies increased future costs, since benefits must be provided over a longer period of time. The underlying risk behind the unexpected improvement in life expectancy is called longevity [...] Read more.
For annuity providers, a higher life expectancy is not always positive news, as it potentially implies increased future costs, since benefits must be provided over a longer period of time. The underlying risk behind the unexpected improvement in life expectancy is called longevity risk. One way to hedge this risk can be attained with the process of securitization through mortality risk securities. This process requires an accurate prediction of the future mortality dynamics with an appropriate mortality model. However, a major issue in mortality modeling is the limited number of available data for a given population. The purpose of this paper is to present a mortality model under the credibility regression framework, aiming to capture the future mortality trends, especially for population datasets of limited available observations. Then, we show how this approach can be incorporated into pricing longevity bonds with the Wang transform. To ensure transparency and applicability in our illustration, the longevity bond pricing is based on the mortality data of Greece. Full article
(This article belongs to the Special Issue Statistics and Quantitative Risk Management for Insurance)
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