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Mathematics
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Information in Economics, Finance and Insurance: Modelling and Applications
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Information in Economics, Finance and Insurance: Modelling and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 12344

Special Issue Editors

Prof. Dr. Alessandra Cretarola

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Perugia, Via Luigi Vanvitelli 1, 06123 Perugia, Italy
Interests: stochastic processes; optimal control; backward stochastic differential equations; mathematical finance; insurance; cryptocurrencies modelling; stochastic modelling of investors’ sentiment
Prof. Dr. Katia Colaneri

E-Mail Website
Guest Editor
Department of Economics and Finance, University of Rome Tor Vergata, Via Columbia 2, 00133 Rome, Italy
Interests: stochastic filtering; stochastic control under full and partial information; backward stochastic differential equations; mathematical finance; insurance

Special Issue Information

Dear Colleagues,

Information is a key element for identifying and quantifying risks in many aspects of our lives, and it assumes a crucial role in fields such as finance and insurance. Developing a reasonable model that can be used as a proxy of real-world risk dynamics is the starting point in any financial and actuarial activity. The goodness or imperfection of models is a consequence of the amount of available information about the nature of the risks and how they are related.

In this Special Issue, we aim to collect review, expository and original papers dealing with problems arising in economics, finance and insurance, where information is the key feature, putting the focus on the role of partial, asymmetric or even private information.

We welcome both theoretical and empirical contributions on information models in economics, finance and insurance; optimal control problems under different information levels; stochastic filtering; insider information; partial and asymmetric information in game theory; risk management; data analysis; learning.

Prof. Dr. Alessandra Cretarola
Prof. Dr. Katia Colaneri
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • Information models
  • Partial information
  • Asymmetric information
  • Insider information
  • Stochastic control
  • Portfolio problems
  • Reinsurance
  • Game theory
  • Data analysis
  • Learning

Published Papers (6 papers)

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Research

24 pages, 882 KiB  
Article
Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective
by Shengkun Xie and Kun Shi
Mathematics 2023, 11(2), 334; https://0-doi-org.brum.beds.ac.uk/10.3390/math11020334 - 09 Jan 2023
Cited by 2 | Viewed by 2280
Abstract
Pricing using a Generalised Linear Model is the gold standard in the auto insurance industry and rate regulation. Generalised Additive Model applications in insurance pricing are receiving increasing attention from academic researchers and actuarial pricing professionals. The actuarial practice has constantly shown evidence [...] Read more.
Pricing using a Generalised Linear Model is the gold standard in the auto insurance industry and rate regulation. Generalised Additive Model applications in insurance pricing are receiving increasing attention from academic researchers and actuarial pricing professionals. The actuarial practice has constantly shown evidence of significantly different premium rates among the different rating territories. In this work, we build predictive models for claim frequency and severity using the synthetic Usage Based Insurance (UBI) dataset variables. First, we conduct territorial clustering based on each location’s claim counts and amounts by grouping those locations into a smaller set, defined as a cluster for rating purposes. After clustering, we incorporate these clusters into our predictive model to determine the risk relativity for each factor level. Through predictive modelling, we have successfully identified key factors that may be helpful for the rate regulation of UBI. Our work aims to fill the gap between individual-level pricing and rate regulation using the UBI database and provides insights on consistency in using traditional rating variables for UBI pricing. Our main contribution is to outline how GAM can address a more complicated functionality of risk factors and the interactions among them. We also contribute to demonstrating the territory clustering problem in UBI to construct the rating territories for pricing and rate regulation. We find that relativity for high annual mileage driven is almost three times that associated with low annual mileage level, which implies its importance in premium calculation. Overall, we provide insights into how UBI can be regulated through traditional pricing factors, additional factors from UBI datasets and rating territories derived from basic rating units and the driver’s location. Full article
(This article belongs to the Special Issue Information in Economics, Finance and Insurance: Modelling and Applications)
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12 pages, 320 KiB  
Article
A Note on the Strong Predictable Representation Property and Enlargement of Filtration
by Antonella Calzolari and Barbara Torti
Mathematics 2022, 10(10), 1783; https://0-doi-org.brum.beds.ac.uk/10.3390/math10101783 - 23 May 2022
Cited by 1 | Viewed by 1386
Abstract
The strong predictable representation property of semi-martingales and the notion of enlargement of filtration meet naturally in modeling financial markets, and theoretical problems arise. Here, first, we illustrate some of them through classical examples. Then, we review recent results obtained by studying predictable [...] Read more.
The strong predictable representation property of semi-martingales and the notion of enlargement of filtration meet naturally in modeling financial markets, and theoretical problems arise. Here, first, we illustrate some of them through classical examples. Then, we review recent results obtained by studying predictable martingale representations for filtrations enlarged by means of a full process, possibly with accessible components in its jump times. The emphasis is on the non-uniqueness of the martingale enjoying the strong predictable representation property with respect to the same enlarged filtration. Full article
(This article belongs to the Special Issue Information in Economics, Finance and Insurance: Modelling and Applications)
13 pages, 884 KiB  
Article
Closing a Bitcoin Trade Optimally under Partial Information: Performance Assessment of a Stochastic Disorder Model
by Zehra Eksi and Daniel Schreitl
Mathematics 2022, 10(1), 157; https://0-doi-org.brum.beds.ac.uk/10.3390/math10010157 - 05 Jan 2022
Cited by 1 | Viewed by 1581
Abstract
The Bitcoin market exhibits characteristics of a market with pricing bubbles. The price is very volatile, and it inherits the risk of quickly increasing to a peak and decreasing from the peak even faster. In this context, it is vital for investors to [...] Read more.
The Bitcoin market exhibits characteristics of a market with pricing bubbles. The price is very volatile, and it inherits the risk of quickly increasing to a peak and decreasing from the peak even faster. In this context, it is vital for investors to close their long positions optimally. In this study, we investigate the performance of the partially observable digital-drift model of Ekström and Lindberg and the corresponding optimal exit strategy on a Bitcoin trade. In order to estimate the unknown intensity of the random drift change time, we refer to Bitcoin halving events, which are considered as pivotal events that push the price up. The out-of-sample performance analysis of the model yields returns values ranging between 9% and 1153%. We conclude that the return of the initiated Bitcoin momentum trades heavily depends on the entry date: the earlier we entered, the higher the expected return at the optimal exit time suggested by the model. Overall, to the extent of our analysis, the model provides a supporting framework for exit decisions, but is by far not the ultimate tool to succeed in every trade. Full article
(This article belongs to the Special Issue Information in Economics, Finance and Insurance: Modelling and Applications)
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23 pages, 605 KiB  
Article
Attracting the Right Crowd under Asymmetric Information: A Game Theory Application to Rewards-Based Crowdfunding
by Francisca Jiménez-Jiménez, Maria Virtudes Alba-Fernández and Cristina Martínez-Gómez
Mathematics 2021, 9(21), 2757; https://0-doi-org.brum.beds.ac.uk/10.3390/math9212757 - 30 Oct 2021
Cited by 2 | Viewed by 2120
Abstract
In this paper, we investigate rewards-based crowdfunding as an innovative financing form for startups and firms. Based on game-theory models under asymmetric information, we test research hypotheses about the positive effects of two main campaign features: funding target and number of rewards. Furthermore, [...] Read more.
In this paper, we investigate rewards-based crowdfunding as an innovative financing form for startups and firms. Based on game-theory models under asymmetric information, we test research hypotheses about the positive effects of two main campaign features: funding target and number of rewards. Furthermore, we examine how and when these characteristics are effective in attracting crowdfunders, by signaling high-quality projects (target) and by pricing according to backers’ preferences (rewards). Conditional process analysis is applied to a dataset of 1613 projects launched on the Spanish platform Verkami from 2015 to 2018. As expected, our study shows that market size is positively influenced by the target and the number of rewards, separately. Further analysis gives some interesting findings. Firstly, we find significant and positive mediating roles of social networks (in the relationship between target and market size) and of backers’ preferences (between rewards and market size). Secondly, the main orientation of a campaign, commercial or social, is relevant to explain previous relationships. While high funding targets are more effective in commercial projects, a high number of rewards is more effective in the social projects. This research provides new insights into the design of optimal crowdfunding, with theoretical and empirical implications. Full article
(This article belongs to the Special Issue Information in Economics, Finance and Insurance: Modelling and Applications)
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17 pages, 961 KiB  
Article
A Square-Root Factor-Based Multi-Population Extension of the Mortality Laws
by Petar Jevtić and Luca Regis
Mathematics 2021, 9(19), 2402; https://doi.org/10.3390/math9192402 - 27 Sep 2021
Cited by 3 | Viewed by 1314
Abstract
In this paper, we present and calibrate a multi-population stochastic mortality model based on latent square-root affine factors of the Cox-Ingersoll and Ross type. The model considers a generalization of the traditional actuarial mortality laws to a stochastic, multi-population and time-varying setting. We [...] Read more.
In this paper, we present and calibrate a multi-population stochastic mortality model based on latent square-root affine factors of the Cox-Ingersoll and Ross type. The model considers a generalization of the traditional actuarial mortality laws to a stochastic, multi-population and time-varying setting. We calibrate the model to fit the mortality dynamics of UK males and females over the last 50 years. We estimate the optimal states and model parameters using quasi-maximum likelihood techniques. Full article
(This article belongs to the Special Issue Information in Economics, Finance and Insurance: Modelling and Applications)
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20 pages, 509 KiB  
Article
Two Approaches for a Dividend Maximization Problem under an Ornstein-Uhlenbeck Interest Rate
by Julia Eisenberg, Stefan Kremsner and Alexander Steinicke
Mathematics 2021, 9(18), 2257; https://0-doi-org.brum.beds.ac.uk/10.3390/math9182257 - 14 Sep 2021
Viewed by 1602
Abstract
We investigate a dividend maximization problem under stochastic interest rates with Ornstein-Uhlenbeck dynamics. This setup also takes negative rates into account. First a deterministic time is considered, where an explicit separating curve α(t) can be found to determine the optimal [...] Read more.
We investigate a dividend maximization problem under stochastic interest rates with Ornstein-Uhlenbeck dynamics. This setup also takes negative rates into account. First a deterministic time is considered, where an explicit separating curve α(t) can be found to determine the optimal strategy at time t. In a second setting, we introduce a strategy-independent stopping time. The properties and behavior of these optimal control problems in both settings are analyzed in an analytical HJB-driven approach, and we also use backward stochastic differential equations. Full article
(This article belongs to the Special Issue Information in Economics, Finance and Insurance: Modelling and Applications)
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