General Insurance Deductible Ratemaking
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چکیده
Insurance claims have deductibles, which must be considered when pricing for insurance premium. Deductibles may cause censoring and truncation to the frequencies, severities, and peril types of observed insurance claims. In practice, the regression approach is often used with deductible amount included as an explanatory variable inside a frequency-severity model, so that the resulting coefficient can be used for an approximate assessment of the relativities for deductibles. This approach is practical, and has the advantage of incorporating the selection effect into deductible ratemaking. On the other hand, standard actuarial textbooks recommend coverage modification approaches, for general insurance ratemaking with deductibles, which requires maximum likelihood estimation of parametric loss models. In this paper, a comprehensive overview of deductibles ratemaking is provided, and the pros and cons of various approaches, under different parametric models, are compared. We demonstrate that the regression approach proves to have an advantage in predicting aggregate claims, when the deductible choice distribution is correlated with the loss frequencies and severities. The maximum likelihood approach proves to be useful when accurate relativities are required for large deductible levels. For demonstration, loss models are fit to the Wisconsin Local Government Property Insurance Fund data, and examples are provided for the ratemaking of per-loss deductibles offered by the fund. Selected parametric models from the generalized beta family distributions are compared. Truncated claim observations of specific
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تاریخ انتشار 2016