We develop several new composite models based on the Weibull distribution for heavy tailed insurance loss data. The composite model assumes different weighted distributions for the head and tail of the distribution and several such models have been introduced in the literature for modeling insurance loss data. For each model proposed in this paper, we specify two parameters as a function of the remaining parameters. These models are fitted to two real insurance loss data sets and their goodness-of-fit is tested. We also present an application to risk measurements and compare the suitability of the models to empirical results.
This paper introduces a new composite model, namely, composite Weibull-Inverse Transformed Gamma distribution which assumes Weibull distribution for the head up to a specified threshold and inverse transformed gamma distribution beyond it. The closed form of probability density function (pdf) as well as the estimation of parameters by maximum likelihood method is presented. The model is compared with several benchmark distributions and their performances are measured. A well-known data set, Danish fire loss data, is used for this purpose and it's Value at Risk (VaR) using the new model is computed. In comparison to several standard models, the composite Weibull-Inverse Transformed Gamma model proved to be a competitor candidate.
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