Modelling outstanding claims liability for workers’ compensation, general liability and automobile liability using Bayesian MCMC simulation
The group adopts the method proposed by Li [2008] to model historical claims run-off data for three lines of business; workers’ compensation, general liability and automobile liability. The group uses one Bayesian model which is similar to the gamma model within the generalized linear models (GLMs)...
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sg-ntu-dr.10356-152592023-05-19T03:30:03Z Modelling outstanding claims liability for workers’ compensation, general liability and automobile liability using Bayesian MCMC simulation Pan, Jin Nyon, Yan Zheng Zheng, Yanxiong Li Ka Ki Jackie Nanyang Business School DRNTU::Business::Finance::Actuarial science The group adopts the method proposed by Li [2008] to model historical claims run-off data for three lines of business; workers’ compensation, general liability and automobile liability. The group uses one Bayesian model which is similar to the gamma model within the generalized linear models (GLMs) and the WinBUGS software to run the MCMC simulation. Then, the group examines the residuals after fitting the model to find the best model for each line of business. Formal model criticism procedures are adopted to refine and adjust the model structures. In the modelling process, the group uses polynomials to smooth parameters from WinBUGS and tackles heteroscedasticity and superimposed inflation. Eventually, the group finds the final adjusted models which are reasonable in both quantitative tests and qualitative reasoning. The detailed tracks of how the group improves the models, conducts relevant tests and comparisons, are shown in the appendices. BUSINESS 2009-04-14T01:40:17Z 2009-04-14T01:40:17Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/15259 en Nanyang Technological University 79 p. application/pdf |
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DRNTU::Business::Finance::Actuarial science Pan, Jin Nyon, Yan Zheng Zheng, Yanxiong Modelling outstanding claims liability for workers’ compensation, general liability and automobile liability using Bayesian MCMC simulation |
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The group adopts the method proposed by Li [2008] to model historical claims run-off data for three lines of business; workers’ compensation, general liability and automobile liability. The group uses one Bayesian model which is similar to the gamma model within the generalized linear models (GLMs) and the WinBUGS software to run the MCMC simulation.
Then, the group examines the residuals after fitting the model to find the best model for each line of business. Formal model criticism procedures are adopted to refine and adjust the model structures. In the modelling process, the group uses polynomials to smooth parameters from WinBUGS and tackles heteroscedasticity and superimposed inflation. Eventually, the group finds the final adjusted models which are reasonable in both quantitative tests and qualitative reasoning. The detailed tracks of how the group improves the models, conducts relevant tests and comparisons, are shown in the appendices. |
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Li Ka Ki Jackie |
author_facet |
Li Ka Ki Jackie Pan, Jin Nyon, Yan Zheng Zheng, Yanxiong |
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Final Year Project |
author |
Pan, Jin Nyon, Yan Zheng Zheng, Yanxiong |
author_sort |
Pan, Jin |
title |
Modelling outstanding claims liability for workers’ compensation, general liability and automobile liability using Bayesian MCMC simulation |
title_short |
Modelling outstanding claims liability for workers’ compensation, general liability and automobile liability using Bayesian MCMC simulation |
title_full |
Modelling outstanding claims liability for workers’ compensation, general liability and automobile liability using Bayesian MCMC simulation |
title_fullStr |
Modelling outstanding claims liability for workers’ compensation, general liability and automobile liability using Bayesian MCMC simulation |
title_full_unstemmed |
Modelling outstanding claims liability for workers’ compensation, general liability and automobile liability using Bayesian MCMC simulation |
title_sort |
modelling outstanding claims liability for workers’ compensation, general liability and automobile liability using bayesian mcmc simulation |
publishDate |
2009 |
url |
http://hdl.handle.net/10356/15259 |
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1770565900247760896 |