Statistical Modelling in Risk Management

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

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 1567

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Electricité de France R&D, 91120 Palaiseau, France
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Special Issue Information

Dear Colleagues,

We invite you to submit papers to this Special Issue "Statistical Modeling in Risk Management". The main objective of this volume is to provide recent developments in the areas of statistical analysis and modelling risks for applications of risk management.

We welcome papers which address statistical analysis of financial data (extreme events, liquidity, volatility, dependences, etc.), forecasting financial data, and the new modelling of financial risks, with applications for portfolio management.

This Special Issue will contain both methodological and empirical papers, as well as applications for portfolio management. The approaches developed may be statistical, machine learning, mathematical finance, operational research, etc. We encourage the sharing of the results of research based data and code sharing.

Dr. Olivier Féron
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

  • statistical modeling
  • risk management
  • financial data analysis extreme events
  • liquidity
  • financial data forecasting
  • financial risk modeling
  • mathematical finance

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Published Papers (1 paper)

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Research

17 pages, 496 KiB  
Article
Dependence Modelling for Heavy-Tailed Multi-Peril Insurance Losses
by Tianxing Yan, Yi Lu and Himchan Jeong
Risks 2024, 12(6), 97; https://doi.org/10.3390/risks12060097 - 16 Jun 2024
Viewed by 1085
Abstract
The Danish fire loss dataset records commercial fire losses under three insurance coverages: building, contents, and profits. Existing research has primarily focused on the heavy-tail behaviour of the losses but ignored the relationship among different insurance coverages. In this paper, we aim to [...] Read more.
The Danish fire loss dataset records commercial fire losses under three insurance coverages: building, contents, and profits. Existing research has primarily focused on the heavy-tail behaviour of the losses but ignored the relationship among different insurance coverages. In this paper, we aim to model the aggregate loss for all three coverages. To study the pairwise dependence of claims from all types of coverage, an independent model, a hierarchical model, and some copula-based models are proposed for the frequency component. Meanwhile, we applied composite distributions to capture the heavy-tailed severity component. It is shown that consideration of dependence for the multi-peril frequencies (i) significantly enhances model goodness-of-fit and (ii) provides more accurate risk measures of the aggregated losses for all types of coverage in total. Full article
(This article belongs to the Special Issue Statistical Modelling in Risk Management)
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