Special Issue "Heavy Tailed Distributions in Economics"

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

Deadline for manuscript submissions: closed (31 July 2018).

Special Issue Editor

Prof. Dr. Dimitrios G. Konstantinides
E-Mail Website
Guest Editor
Department Statistics and Actuarial—Financial Mathematics, University of the Aegean, GR 83200 Samos, Greece
Interests: risk theory; actuarial science; macroeconomics
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Special Issue Information

Dear Colleagues,

The heavy tailed distributions are not the only source of economic instability, but they can be modelled and handled using mathematical tools only. This feature makes them a hot topic in insurance practice in general and especially in regulatory institutions. Its interaction with the dependence issues is a usual complication, which produce many fruitful considerations.

The special issue aims to compile any paper with significant contribution in the state-of-the-art or in the facilitation of the practical implementation of the theoretical approach.

Prof. Dr.  Dimitrios Konstantinides
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 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 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

  • renewal risk model
  • asymptotic formulas
  • ruin probability
  • dependence modelling
  • optimization procedures

Published Papers (2 papers)

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Research

Article
Linear Regression for Heavy Tails
Risks 2018, 6(3), 93; https://0-doi-org.brum.beds.ac.uk/10.3390/risks6030093 - 10 Sep 2018
Cited by 3 | Viewed by 1319
Abstract
There exist several estimators of the regression line in the simple linear regression: Least Squares, Least Absolute Deviation, Right Median, Theil–Sen, Weighted Balance, and Least Trimmed Squares. Their performance for heavy tails is compared below on the basis of a quadratic loss function. [...] Read more.
There exist several estimators of the regression line in the simple linear regression: Least Squares, Least Absolute Deviation, Right Median, Theil–Sen, Weighted Balance, and Least Trimmed Squares. Their performance for heavy tails is compared below on the basis of a quadratic loss function. The case where the explanatory variable is the inverse of a standard uniform variable and where the error has a Cauchy distribution plays a central role, but heavier and lighter tails are also considered. Tables list the empirical sd and bias for ten batches of one hundred thousand simulations when the explanatory variable has a Pareto distribution and the error has a symmetric Student distribution or a one-sided Pareto distribution for various tail indices. The results in the tables may be used as benchmarks. The sample size is n = 100 but results for n = are also presented. The error in the estimate of the slope tneed not be asymptotically normal. For symmetric errors, the symmetric generalized beta prime densities often give a good fit. Full article
(This article belongs to the Special Issue Heavy Tailed Distributions in Economics)
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Article
Precise Large Deviations for Subexponential Distributions in a Multi Risk Model
Risks 2018, 6(2), 27; https://0-doi-org.brum.beds.ac.uk/10.3390/risks6020027 - 27 Mar 2018
Viewed by 1136
Abstract
The precise large deviations asymptotics for the sums of independent identical random variables when the distribution of the summand belongs to the class S of heavy tailed distributions is studied. Under mild conditions, we extend the previous results from the paper Denisov [...] Read more.
The precise large deviations asymptotics for the sums of independent identical random variables when the distribution of the summand belongs to the class S of heavy tailed distributions is studied. Under mild conditions, we extend the previous results from the paper Denisov et al. (2010) to asymptotics that are valid uniformly over some time interval. Finally, we apply the main result on the multi-risk model introduced by Wang and Wang (2007). Full article
(This article belongs to the Special Issue Heavy Tailed Distributions in Economics)
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