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Statistical distributions in general insurance stochastic processes

Dissertation (MSc)--University of Pretoria, 2014.

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Other Authors: Fabris-Rotelli, Inger Nicolette
Format: Thesis
Language:English
Published: University of Pretoria 2015
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access_status_str Open Access
author2 Fabris-Rotelli, Inger Nicolette
author_browse Fabris-Rotelli, Inger Nicolette
author_facet Fabris-Rotelli, Inger Nicolette
collection Thesis
dc_rights_str_mv © 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Dissertation (MSc)--University of Pretoria, 2014.
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institution University of Pretoria (South Africa)
language English
last_indexed 2026-06-10T12:38:16.420Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2015
publishDateRange 2015
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publisher University of Pretoria
publisherStr University of Pretoria
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spelling oai:repository.up.ac.za:2263/43253 Statistical distributions in general insurance stochastic processes Fabris-Rotelli, Inger Nicolette rsteenkamp@deloitte.co.za Steenkamp, Jan Hendrik Harm UCTD Dissertation (MSc)--University of Pretoria, 2014. A general insurance risk model consists of in initial reserve, the premiums collected, the return on investment of these premiums, the claims frequency and the claims sizes. Except for the initial reserve, these components are all stochastic. The assumption of the distributions of the claims sizes is an integral part of the model and can greatly in uence decisions on reinsurance agreements and ruin probabilities. An array of parametric distributions are available for use in describing the distribution of claims. The study is focussed on parametric distributions that have positive skewness and are de ned for positive real values. The main properties and parameterizations are studied for a number of distributions. Maximum likelihood estimation and method-of-moments estimation are considered as techniques for tting these distributions. Multivariate numerical maximum likelihood estimation algorithms are proposed together with discussions on the e ciency of each of the estimation algorithms based on simulation exercises. These discussions are accompanied with programs developed in SAS PROC IML that can be used to simulate from the various parametric distributions and to t these parametric distributions to observed data. The presence of heavy upper tails in the context of general insurance claims size distributions indicates that there exists a high risk of observing very large and even extreme claims. This needs to be allowed for in the modeling of claims. Methods used to describe tail weight together with techniques that can be used to detect the presence of heavy upper tails are studied. These methods are then applied to the parametric distributions to classify their tails' heaviness. The study is concluded with an application of the techniques developed to t the parametric distributions and to evaluate the tail heaviness of reallife claims data. The goodness-of- t of the various tted distributions are discussed. Based on the nal results further research topics are identi ed. lk2014 Statistics MSc Unrestricted 2015-01-19T12:13:17Z 2015-01-19T12:13:17Z 2014/12/12 2014 Dissertation Steenkamp, JHH 2014, Statistical distributions in general insurance stochastic processes, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/43253> M14/9/223 http://hdl.handle.net/2263/43253 en © 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria
spellingShingle UCTD
Statistical distributions in general insurance stochastic processes
title Statistical distributions in general insurance stochastic processes
title_full Statistical distributions in general insurance stochastic processes
title_fullStr Statistical distributions in general insurance stochastic processes
title_full_unstemmed Statistical distributions in general insurance stochastic processes
title_short Statistical distributions in general insurance stochastic processes
title_sort statistical distributions in general insurance stochastic processes
topic UCTD
url http://hdl.handle.net/2263/43253