Special Issue "Heavy-Tail Phenomena in Insurance, Finance, and Other Related Fields"

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

Deadline for manuscript submissions: 31 December 2019

Special Issue Editors

Guest Editor
Prof. Dr. Yiqing Chen

Drake University, Des Moines, IA 50311, USA
Website | E-Mail
Interests: extreme risks in insurance and finance; quantitative risk management; insurance solvency analysis
Guest Editor
Prof. Dr. Xiaohu Li

Stevens Institute of Technology, Hoboken, NJ 07030, USA
Website | E-Mail
Interests: dependence modelling; stochastic orders; quantitative risk management

Special Issue Information

Dear Colleagues,

The journal Risks has launched a Special Issue on “Heavy-Tail Phenomena in Insurance, Finance, and Other Related Fields” in conjunction with the Workshop on Heavy-Tail Phenomena and the Workshop on Insurance and Financial Risks held in China in June 2018.

Heavy-tail phenomena, existing everywhere and usually taking the form of natural or man-made catastrophes, have attracted much attention from academics and practitioners in insurance, finance, information science, and environmental science. The 4th International Workshop on Statistical Modeling of Heavy-Tail Phenomena with Applications, hosted by Xi’an Jiaotong-Liverpool University, 1–4 June 2018, is an important platform for exchanging research ideas and disseminating recent advances on this topic. Keynote speakers are: Thomas Mikosch (University of Copenhagen), Sidney Resnick (Cornell University), Gennady Samorodnitsky (Cornell University), and Qihe Tang (University of Iowa and University of New South Wales).

Interplay of insurance and financial risks is a newly emerging research topic in the interface of insurance, finance, and risk management. It is in accordance with the contemporary trends that insurers make risky investments to optimize profits and investors buy insurance against catastrophic losses, making them exposed to both insurance and financial risks. The International Workshop on Risks in Insurance and Finance, hosted by Northwest Normal University, 7–9 June 2018, offers an exciting gathering of the leading researchers in these fields. Keynote speakers are: Darinka Dentcheva (Stevens Institute of Technology), Harry Joe (University of British Columbia), and Etienne Marceau (Université Laval).

We welcome all participants of the two workshops to submit their manuscripts to this Special Issue. All manuscripts will be refereed through the same peer-review process of the journal.

Prof. Dr. Yiqing Chen
Prof. Dr. Xiaohu Li
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 papers will be 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 quarterly 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 350 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

  • Heavy tail
  • Insurance risk
  • Financial risk
  • Statistical modeling
  • Dependence
  • Stochastic optimization
  • Risk management
  • Extremes

Published Papers (3 papers)

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Research

Open AccessArticle Robust Estimations for the Tail Index of Weibull-Type Distribution
Received: 1 September 2018 / Revised: 30 September 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
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Abstract
Based on suitable left-truncated or censored data, two flexible classes of M-estimations of Weibull tail coefficient are proposed with two additional parameters bounding the impact of extreme contamination. Asymptotic normality with n-rate of convergence is obtained. Its robustness is discussed via [...] Read more.
Based on suitable left-truncated or censored data, two flexible classes of M-estimations of Weibull tail coefficient are proposed with two additional parameters bounding the impact of extreme contamination. Asymptotic normality with n -rate of convergence is obtained. Its robustness is discussed via its asymptotic relative efficiency and influence function. It is further demonstrated by a small scale of simulations and an empirical study on CRIX. Full article
(This article belongs to the Special Issue Heavy-Tail Phenomena in Insurance, Finance, and Other Related Fields)
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Open AccessArticle Some Results on Measures of Interaction between Paired Risks
Received: 2 August 2018 / Revised: 24 August 2018 / Accepted: 27 August 2018 / Published: 27 August 2018
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Abstract
Co-risk measures and risk contribution measures have been introduced to evaluate the degree of interaction between paired risks in actuarial risk management. This paper attempts to study the ordering behavior of measures on interaction between paired risks. For various co-risk measures and risk [...] Read more.
Co-risk measures and risk contribution measures have been introduced to evaluate the degree of interaction between paired risks in actuarial risk management. This paper attempts to study the ordering behavior of measures on interaction between paired risks. For various co-risk measures and risk contribution measures, we investigate how the marginal distributions and the dependence structure impact on the level of interaction between paired risks. Also, several numerical examples based on Monte Carlo simulation are presented to illustrate the main findings. Full article
(This article belongs to the Special Issue Heavy-Tail Phenomena in Insurance, Finance, and Other Related Fields)
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Open AccessArticle Bayesian Adjustment for Insurance Misrepresentation in Heavy-Tailed Loss Regression
Received: 24 July 2018 / Revised: 9 August 2018 / Accepted: 10 August 2018 / Published: 17 August 2018
Cited by 1 | PDF Full-text (1043 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we study the problem of misrepresentation under heavy-tailed regression models with the presence of both misrepresented and correctly-measured risk factors. Misrepresentation is a type of fraud when a policy applicant gives a false statement on a risk factor that determines [...] Read more.
In this paper, we study the problem of misrepresentation under heavy-tailed regression models with the presence of both misrepresented and correctly-measured risk factors. Misrepresentation is a type of fraud when a policy applicant gives a false statement on a risk factor that determines the insurance premium. Under the regression context, we introduce heavy-tailed misrepresentation models based on the lognormal, Weibull and Pareto distributions. The proposed models allow insurance modelers to identify risk characteristics associated with the misrepresentation risk, by imposing a latent logit model on the prevalence of misrepresentation. We prove the theoretical identifiability and implement the models using Bayesian Markov chain Monte Carlo techniques. The model performance is evaluated through both simulated data and real data from the Medical Panel Expenditure Survey. The simulation study confirms the consistency of the Bayesian estimators in large samples, whereas the case study demonstrates the necessity of the proposed models for real applications when the losses exhibit heavy-tailed features. Full article
(This article belongs to the Special Issue Heavy-Tail Phenomena in Insurance, Finance, and Other Related Fields)
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