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Risks
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Advances in Risk Models and Actuarial Science
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Advances in Risk Models and Actuarial Science

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1368

Special Issue Editor

Dr. Rui Manuel Rodrigues Cardoso

E-Mail Website
Guest Editor
1. Center for Mathematics and Applications (NOVA Math), Universidade NOVA de Lisboa (FCT NOVA), Caparica, Portugal
2. Department of Mathematics, Faculty of Science and Technology, Universidade NOVA de Lisboa (FCT NOVA), Caparica, Portugal
Interests: actuarial mathematics; risk theory; ruin theory; classical risk model; dual risk model; bonus malus systems for car insurance; variance calculation in life insurance; ruin probabilities in the context of the winner’s curse; ruin under independent, randomised observations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Actuarial science is an important branch of applied mathematics, in the context of insurance markets, using countless theoretical advancements in mathematics that have led to successful applications in risk management and the development of insurance risk models. The field faces dynamic challenges, such as climate change, cybersecurity threats, shifting social behaviours, and demographic changes. At the same time, advancements in computing technologies and the development of innovative techniques in areas like big data analysis, machine learning, data analytics, and artificial intelligence are creating exciting new research opportunities. Actuaries are now tasked with tackling a wide range of newly emerging risks. These challenges require integrating novel risks into existing models or developing new assessment methodologies, offering deeper insights into insurance risk modelling.

This Special Issue serves as an invitation to researchers and professionals from both academia and industry to share their groundbreaking contributions, helping to enrich and advance this multidisciplinary field.

You may choose our Joint Special Issue in Mathematics.

Dr. Rui Manuel Rodrigues Cardoso
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

  • climate change in insurance and sustainability
  • cyber security risks
  • data science for insurance
  • reinsurance
  • non-life insurance mathematics
  • fraud detection in insurance
  • telematics data analysis for insurance
  • estimation and evaluation of risk management models
  • loss reserving
  • risk theory

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

Published Papers (3 papers)

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Research

22 pages, 1833 KiB  
Article
Survival Analysis for Credit Risk: A Dynamic Approach for Basel IRB Compliance
by Fernando L. Dala, Manuel L. Esquível and Raquel M. Gaspar
Risks 2025, 13(8), 155; https://doi.org/10.3390/risks13080155 - 15 Aug 2025
Viewed by 167
Abstract
This paper uses survival analysis as a tool to assess credit risk in loan portfolios within the framework of the Basel Internal Ratings-Based (IRB) approach. By modeling the time to default using survival functions, the methodology allows for the estimation of default probabilities [...] Read more.
This paper uses survival analysis as a tool to assess credit risk in loan portfolios within the framework of the Basel Internal Ratings-Based (IRB) approach. By modeling the time to default using survival functions, the methodology allows for the estimation of default probabilities and the dynamic evaluation of portfolio performance. The model explicitly accounts for right censoring and demonstrates strong predictive accuracy. Furthermore, by incorporating additional information about the portfolio’s loss process, we show how to empirically estimate key risk measures—such as Value at Risk (VaR) and Expected Shortfall (ES)—that are sensitive to the age of the loans. Through simulations, we illustrate how loss distributions and the corresponding risk measures evolve over the loans’ life cycles. Our approach emphasizes the significant dependence of risk metrics on loan age, illustrating that risk profiles are inherently dynamic rather than static. Using a real-world dataset of 10,479 loans issued by Angolan commercial banks, combined with assumptions regarding loss processes, we demonstrate the practical applicability of the proposed methodology. This approach is particularly relevant for emerging markets with limited access to advanced credit risk modeling infrastructure. Full article
(This article belongs to the Special Issue Advances in Risk Models and Actuarial Science)
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12 pages, 1125 KiB  
Article
Algorithmic Trading System with Adaptive State Model of a Binary-Temporal Representation
by Michal Dominik Stasiak
Risks 2025, 13(8), 148; https://doi.org/10.3390/risks13080148 - 4 Aug 2025
Viewed by 337
Abstract
In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise [...] Read more.
In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of exchange rates without losing any informative value of the data. The basis of the model is the trajectory analysis for the ensuing changes in price quotations and dependencies between the duration of each change. The main advantage of the model is to eliminate the threshold analysis, used in existing state models. This solution allows for a more accurate identification of investor behavior patterns, which translates into a reduction of investment risk. In order to verify obtained results in practice, the paper presents a concept of creating an algorithmic trading system and an analysis of its financial effectiveness for the exchange rate most popular among investors, namely EUR/USD. Full article
(This article belongs to the Special Issue Advances in Risk Models and Actuarial Science)
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22 pages, 426 KiB  
Article
Uncovering Systemic Risk in ASEAN Corporations: A Framework Based on Graph Theory and Hidden Models
by Marc Cortés Rufé, Jordi Martí Pidelaserra and Cecilia Kindelán Amorrich
Risks 2025, 13(5), 95; https://doi.org/10.3390/risks13050095 - 13 May 2025
Viewed by 588
Abstract
In the context of an ever-evolving global economy, ASEAN companies face dynamic systemic risk that reshapes their financial interrelationships. This study examines the transmission of these risks using advanced graph theory techniques, particularly the measurement of eigenvector centrality based on Euclidean distances, combined [...] Read more.
In the context of an ever-evolving global economy, ASEAN companies face dynamic systemic risk that reshapes their financial interrelationships. This study examines the transmission of these risks using advanced graph theory techniques, particularly the measurement of eigenvector centrality based on Euclidean distances, combined with a hidden model that incorporates macroeconomic variables, such as GDP. The research focuses on identifying critical nodes within the corporate network, evaluating their contagion potential—both in terms of reinforcing resilience and amplifying vulnerabilities—and analyzing the influence of external factors on the network’s structure and behavior. The findings offer an innovative framework for managing systemic risk and provide strategic guidelines for the formulation of economic policies in emerging ASEAN markets. Full article
(This article belongs to the Special Issue Advances in Risk Models and Actuarial Science)
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