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Big Data and Complex Networks in Finance and Insurance
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Big Data and Complex Networks in Finance and Insurance

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: 1 August 2024 | Viewed by 1334

Special Issue Editors

Dr. Gian Paolo Clemente

E-Mail Website
Guest Editor
Department of Mathematics for Economic, Financial and Actuarial Sciences, Catholic University of Milan, 20123 Largo Gemelli 1, Milan, Italy
Interests: actuarial science; complex networks
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandra Cornaro

E-Mail Website
Guest Editor
Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy
Interests: complex systems; network theory

Special Issue Information

Dear Colleagues,

The field of finance and insurance has witnessed an important transformation over the past few decades with the advent of complex network and data science techniques. The growing availability of large-scale financial and insurance data, coupled with the advancement of complex network analysis and machine learning algorithms, has opened up new possibilities for researchers and practitioners to gain deeper insights into financial and insurance markets, and to develop more accurate and efficient models for predicting market behaviour and managing risks.

This Special Issue aims to bring together cutting-edge research on the use of complex network and data science techniques in finance and insurance. The Special Issue will feature a wide range of topics, including network-based analysis of financial markets, machine learning approaches for evaluating risk in insurance, risk management using big data analytics, and many others. The Special Issue will also explore the potential applications of these techniques in areas such as insurance pricing, fraud detection, and portfolio management.

The contributions in this Special Issue will showcase the latest research and developments in the field of finance and insurance, highlighting the importance of interdisciplinary approaches that combine insights from computer science, mathematics, and economics. The Special Issue will be of interest to academics, researchers, and practitioners in the field, as well as to those who are interested in the potential applications of complex network and data science techniques in finance and insurance.

Dr. Gian Paolo Clemente
Dr. Alessandra Cornaro
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. Journal of Risk and Financial Management 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 1400 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
  • finance
  • complex network
  • machine learning
  • multilayer network

Published Papers (2 papers)

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Research

17 pages, 385 KiB  
Article
A Discrete Risk-Theory Approach to Manage Equity-Linked Policies in an Incomplete Market
by Francesco Della Corte and Francesca Marzorati
J. Risk Financial Manag. 2024, 17(4), 158; https://doi.org/10.3390/jrfm17040158 - 14 Apr 2024
Viewed by 438
Abstract
We construct a model where, at each time instance, risky securities can only take a limited number of values and the equity-linked policy sold by the insurer to policyholders pays benefits linked to these securities. Since the number of states in the model [...] Read more.
We construct a model where, at each time instance, risky securities can only take a limited number of values and the equity-linked policy sold by the insurer to policyholders pays benefits linked to these securities. Since the number of states in the model exceeds the number of securities in the (incomplete) market, the martingale measure is not unique, posing a problem in pricing insurance instruments. In this framework, we consider how a super-replicating strategy violates the assumption of absence of arbitrage, yet simultaneously allows the insurance company to fully hedge against financial risk. Since the super-replicating strategy, when considered alone, would be too costly for any rational insured person, through the definition of the safety loading, we demonstrate how the insurer can still hedge against financial risk, albeit at the expense of increasing its exposure to demographic risk. This approach does not aim to show how the pricing of the index-linked policy can actually be performed but rather highlights how risk theory-based approaches (via the definition of the profit and loss random variable) enable the management of the trade-off between financial risk and demographic risk. Full article
(This article belongs to the Special Issue Big Data and Complex Networks in Finance and Insurance)
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34 pages, 6169 KiB  
Article
A Solvency II Partial Internal Model Considering Reinsurance and Counterparty Default Risk
by Matteo Crisafulli
J. Risk Financial Manag. 2024, 17(4), 148; https://doi.org/10.3390/jrfm17040148 - 06 Apr 2024
Viewed by 567
Abstract
Estimating the expected capital and its variability is a crucial objective for a non-life insurance company, which enables the firm to develop effective management strategies. Many studies have been devoted to this topic, with simulative approaches being especially employed for solving the complexity [...] Read more.
Estimating the expected capital and its variability is a crucial objective for a non-life insurance company, which enables the firm to develop effective management strategies. Many studies have been devoted to this topic, with simulative approaches being especially employed for solving the complexity of the interacting risks, not manageable through closed-form solutions. In this paper, we present a realistic framework based on Solvency II for the definition of next-year capital of a non-life insurer, including reinsurance treaties and counterparty default risk, in a multi-line of business setting. We determine the mean and variance of the stochastic capital considering both quota share and excess-of-loss reinsurance. We show how these closed-form results enable the analysis of many different real-world strategies, granting the insurer the possibility of choosing the optimal policy without the computational resources and time constraints required by simulative approaches. Full article
(This article belongs to the Special Issue Big Data and Complex Networks in Finance and Insurance)
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