risk margin

An individual risk model for premium calculation based on quantile: a comparison between Generalized Linear Models and Quantile Regression

This paper deals with the use of quantile regression and generelized linear models for a premium calculation based on quantiles. A premium principle is a functional that assigns a usually loaded premium to any distribution of claims. The loaded premium is generally greater than the expected value of the loss and the difference is considered to be a risk margin or a safety loading. The failure of a right charge of individual risk rate exposes the insurer to adverse selection, and consequently, to deteriorating financial results.

An application of parametric quantile regression to extend the two-stage quantile regression for ratemaking

This paper deals with the use of parametric quantile regression for the calculation of a loaded premium, based on a quantile measure, corresponding to individual insurance risk. Heras et al. have recently introduced a ratemaking process based on a two-stage quantile regression model. In the first stage, a probability to have at least one claim is estimated by a GLM logit, whereas in the second stage several quantile regressions are necessary for the estimate of the severity component.

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