Selected Papers from the Actuarial Risk Modelling and Extreme Values Workshop

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

Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 10107

Special Issue Editors

Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra 0200, Australia
Interests: actuarial sciences; applied statistics; quantitative finance
Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra 0200, Australia
Interests: probability; statistics; insurance and quantitative finance; quantitative criminology (recidivism studies)
Faculty of Economics and Social Sciences, University of Hamburg, 20146 Hamburg, Germany
Interests: financial econometrics; mathematical finance; economics of risk and insurance
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Special Issue Information

Dear Colleagues,

Actuaries face many challenges today, especially relating to such topical issues as the analysis of extremely large data sets and high-dimensional data on the one hand, through to risks of extreme events, increased longevity, and mortality modelling and forecasting at extreme ages, on the other. These challenges are highly relevant for today's insurance industry, bringing imperatives and incentives for researchers to develop new tools for risk modelling and risk management.

The Research School of Finance, Actuarial Studies and Statistics, College of Business and Economics, Australian National University, is convening a two-day workshop on the 6th and 7th September 2018 with the intention of bringing together experts in these areas from different disciplines to present up-to-date reviews and overviews of their subjects.  The workshop will provide a unique opportunity for academics and practitioners to meet and discuss these important actuarial problems and their solutions. For more details, please refer to the workshop webpage: https://www.rsfas.anu.edu.au/rsfas-research/workshop-series/.

We welcome all participants to submit their manuscripts presented at the workshop to this special issue. All manuscripts will be refereed through the same peer-review process of the journal Risks.

Dr. Fei Huang
Prof. Dr. Ross Maller
Prof. Dr. Alexander Szimayer
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. 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

  • Extreme value theory
  • Aging and longevity
  • Catastrophe risks
  • Financial risk management
  • Predictive analytics
  • Measures of risk
  • Modelling excess losses
  • Dependence modelling and copulas

Published Papers (2 papers)

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Research

22 pages, 1179 KiB  
Article
Direct and Hierarchical Models for Aggregating Spatially Dependent Catastrophe Risks
Risks 2019, 7(2), 54; https://doi.org/10.3390/risks7020054 - 08 May 2019
Cited by 1 | Viewed by 5845
Abstract
We present several fast algorithms for computing the distribution of a sum of spatially dependent, discrete random variables to aggregate catastrophe risk. The algorithms are based on direct and hierarchical copula trees. Computing speed comes from the fact that loss aggregation at branching [...] Read more.
We present several fast algorithms for computing the distribution of a sum of spatially dependent, discrete random variables to aggregate catastrophe risk. The algorithms are based on direct and hierarchical copula trees. Computing speed comes from the fact that loss aggregation at branching nodes is based on combination of fast approximation to brute-force convolution, arithmetization (regriding) and linear complexity of the method for computing the distribution of comonotonic sum of risks. We discuss the impact of tree topology on the second-order moments and tail statistics of the resulting distribution of the total risk. We test the performance of the presented models by accumulating ground-up loss for 29,000 risks affected by hurricane peril. Full article
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15 pages, 1074 KiB  
Article
Mortality Forecasting: How Far Back Should We Look in Time?
Risks 2019, 7(1), 22; https://doi.org/10.3390/risks7010022 - 22 Feb 2019
Cited by 2 | Viewed by 3805
Abstract
Extrapolative methods are one of the most commonly-adopted forecasting approaches in the literature on projecting future mortality rates. It can be argued that there are two types of mortality models using this approach. The first extracts patterns in age, time and cohort dimensions [...] Read more.
Extrapolative methods are one of the most commonly-adopted forecasting approaches in the literature on projecting future mortality rates. It can be argued that there are two types of mortality models using this approach. The first extracts patterns in age, time and cohort dimensions either in a deterministic fashion or a stochastic fashion. The second uses non-parametric smoothing techniques to model mortality and thus has no explicit constraints placed on the model. We argue that from a forecasting point of view, the main difference between the two types of models is whether they treat recent and historical information equally in the projection process. In this paper, we compare the forecasting performance of the two types of models using Great Britain male mortality data from 1950–2016. We also conduct a robustness test to see how sensitive the forecasts are to the changes in the length of historical data used to calibrate the models. The main conclusion from the study is that more recent information should be given more weight in the forecasting process as it has greater predictive power over historical information. Full article
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