Prediction of Outstanding Liabilities in Non-Life Insurance

Norberg, Ragnar

doi:10.2143/ast.23.1.2005103

Cited by 142 publications

(99 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An example would be to incorporate claim severities. This could be done by extend-515 ing the counting process set-up of this paper to the marked point processes approach (Norberg, 1993). This could also help to generalise the recent double chain-ladder technique of Verrall et al (2010), Martínez-Miranda et al (2011) and Martínez-Miranda et al (2012) to continuous time.…”

mentioning

confidence: 87%

See 1 more Smart Citation

In-sample forecasting with local linear survival densities

et al. 2016

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. In this paper, in-sample forecasting is defined as forecasting a structured density to sets where it is unobserved. The structured density consists of one-dimensional in-sample components that identify the density on such sets. We focus on the multiplicative density structure, which has recently been seen as the underlying structure of non-life insurance forecasts. In non-life insurance the in-sample area is defined as one triangle and the forecasting area as the triangle that 20 added to the first triangle produces a square. Recent approaches estimate two one-dimensional components by projecting an unstructured two-dimensional density estimator onto the space of multiplicatively separable functions. We show that time-reversal reduces the problem to two onedimensional problems, where the one-dimensional data are left-truncated and a one-dimensional survival density estimator is needed. This paper then uses the local linear density smoother with 25 weighted cross-validated and do-validated bandwidth selectors. Full asymptotic theory is provided, with and without time reversal. Finite sample studies and an application to non-life insurance are included. Permanent repository link

show abstract

mentioning

confidence: 87%

“…The most favourable approach depends on 125 the application and situation. For approaches that study reserving for outstanding liabilities and that work with exposure in forward-moving time see Arjas (1989), Norberg (1993) and Antonio & Plat (2014).…”

mentioning

confidence: 99%

In-sample forecasting with local linear survival densities

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Com a evolução da capacidade computacional, England e Verrall (2002) também acreditam que se torna questionável examinar os dados agregados e assim perder informações importantes que poderiam ser utilizadas para produzir uma melhor estimativa e ajudar a associar uma distribuição a esta reserva. Norberg (1993), inspirado em Karlsson (1976), Arjas (1989) e Jewell (1989 propõe um processo Poisson marcado não homogêneo: os sinistros seguem um processo homogêneo e uma marca aleatória é associada a cada sinistro, representando seu desenvolvimento da ocorrência até o encerramento final.…”

Section: Revisão Bibliográficaunclassified

“…Esta estrutura exposta por Parodi inspira o modelo que será utilizado nesta dissertação, sendo o foco nos 2 primeiros componentes para obter a IBNR pura. Nele é estabelecida uma estrutura para a frequência que os sinistros ocorrem e atraso do aviso dos sinistros, mas não modela o processo de pagamento no nível de sinistros individuais como Haastrup e Arjas(1997) e Norberg (1993Norberg ( , 1999, e sim o montante de pagamento esperado.…”

Section: 22unclassified

Um Modelo Poisson-Lognormal Para Previsão Da Quantidade Ibnr via Micro-Dados

MACEDO¹

View full text Add to dashboard Cite

Macedo, Juliana Fernandes da Costa; Veiga Filho, Álvaro de Lima (Advisor). A Poisson-Lognormal model to forecast the IBNR quantity via micro-data. Rio de Janeiro, 2015. 81p. MSc DissertationDepartamento de Engenharia Elétrica, Pontifícia Universidade Católica do Rio de Janeiro.The main objective of this dissertation is to predict the IBNR reserve. For this, it was developed a statistical model of combined distributions looking for a new distribution that fits the data well. The IBNR reserve, short for "Incurred But Not Reported", represents the amount that insurers need to have to pay for the claims that occurred in the past but have not been reported until the present date.Given the importance of this reserve, several methods for estimating this reserve have been proposed. One of the most used methods for the insurers is the Chain Ladder, which is based on run-off triangles; this is a format of grouping the data according to the occurrence and the reported date. However this format causes the lost of important information. This dissertation, based on other articles and works that consider the data not grouped, proposes a new model for the non-aggregated data. The proposed model combines the delay in the claim report distribution represented by a log normal truncated (because there is only information until the last observed date); the total amount of claims incurred in a given period modeled by a Poisson distribution and the number of claims occurred in a certain period and reported until the last observed date characterized by a binomial distribution.Finally, the IBNR reserve was estimated by this method and by the chain ladder and the prediction capacity of both methods will be evaluated. Although the delay distribution seems to fit the data well, the proposed model obtained inferior results to the Chain Ladder in terms of forecast.

show abstract

“…Assim como nas abordagens de Arjas(28) e Norberg (25), o processo de reivindicação de sinistrosé tratado como um Processo Poisson Marcado com Posição Dependente. Um pontoé o instante de tempo de ocorrência de um sinistro e a marca associadaé a combinação do atraso no aviso e desenvolvimento do sinistro.…”

Section: Processo Poisson Marcado Com Posição Dependenteunclassified

Comparação De Métodos De Micro-Dados E De Triângulo Run-Off Para Previsão Da Quantidade Ibnr

SOUZA¹

View full text Add to dashboard Cite

Gomes de Souza, Luciene; de Lima Veiga Filho,Álvaro(Orientador).Comparison of methods of micro-data and run-off triangle for prediction amount of IBNR. Rio de Janeiro, 2013. 83p. MSc. Dissertation -Departamento de Engenharia Elétrica, Pontifícia Universidade Católica do Rio de Janeiro.The IBNR reserve is a reserve of paramount importance for insurers. Its calculation has been accomplished by methods, mostly, deterministic, traditionally applied to claims grouped information in a particular format called run-off triangle . This method of calculation was very adequate for decades because of its simplicity and the limited computational processing capacity existing in the past. Today, with the breakthrough of this capacity, no waiver to investigating relevant information that may be lost with grouping data would be need. Many flaws of the traditional methods has been mentioned in the literature and the use of detailed information has been pointed as a form of overcoming these deficiencies. Another frequent aim in methodologies proposed for the calculation of IBNR is get a good measure of the accuracy of the estimates obtained by them and that is another expectation about the use of detailed data, since if you got more data you could get better measures.Inspired by some articles already published with proposals for modeling such not grouped data, this dissertation proposes a new model and evaluate its predictive ability and gain of knowledge about the process of occurrence and notice of the claim against that one can get from the traditional methods applied to data of amount of claims for obtain the amount of IBNR claims and their distribution. IntroduçãoA sigla IBNR (Incurred but not reported) identifica uma importante reserva que deve ser constituída pelas companhias de seguro em geral em todos os seguimentos. Esta reserva, em um determinado instante de tempo t, deve corresponder ao montante necessário para o pagamento de indenizações de todos os sinistros já ocorridos, porém ainda não informadosà seguradora.Portanto, a IBNRé definida através de uma estimativa dos sinistros ainda não observados.Rob Kaas et al.(1) afirmam que há décadas atrás, as carteiras de seguros não-vida eram financiadas através de um sistema "pay-as-you-go". Todas as reivindicações em um determinado ano eram pagas a partir da receita de prêmios do mesmo ano, independentemente do ano de origem do sinistro. Ganhos e perdas técnicas surgiram devidoà diferença entre a receita de prêmios em um ano e os sinistros pagos durante o ano. A IBNR busca antecipar o custo dos sinistros de responsabilidade da seguradora, porém ainda não conhecidos, reduzindo essas perdas e ganhos, e assim fazendo com que balanço financeiro do portfólio de cada ano seja mais próximo do real e sempre haja recursos para cobrir as indenizações a serem pagas. Diversos autores enfatizam a importância da precisão da estimativa da reserva IBNR. Segundo Friedland(2) podemos ver a importância da acurácia da estimativa de sinistros não pagos em geral por 3 pontos de vista, a ...

show abstract

Prediction of Outstanding Liabilities in Non-Life Insurance

Cited by 142 publications

References 10 publications

In-sample forecasting with local linear survival densities

In-sample forecasting with local linear survival densities

Um Modelo Poisson-Lognormal Para Previsão Da Quantidade Ibnr via Micro-Dados

Comparação De Métodos De Micro-Dados E De Triângulo Run-Off Para Previsão Da Quantidade Ibnr

Contact Info

Product

Resources

About