Classification Ratemaking via Quantile Regression and a Comparison with Generalized Linear Models

Fabio Baione, Davide Biancalana, Paolo De Angelis and Ivan Granito
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Fabio Baione: Catholic University, Department of Mathematical Sciences, Mathematical Finance and Econometrics
Davide Biancalana: Sapienza University of Rome, Department of Statistical Sciences
Paolo De Angelis: Sapienza University of Rome, Department of Methods and Models for Economics Territory and Finance
Ivan Granito: Sapienza University of Rome, Department of Statistical Sciences

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 87-91 from Springer

Abstract: Abstract In non-life insurance, it is important to develop a loaded premium for individual risks, as the sum of a pure premium (expected value of loss) and a safety loading or risk margin. In actuarial practice, this process is known as classification ratemaking and is performed usually via Generalized Linear Model. The latter permits an estimate of individual pure premium and safety loading both; however, the goodness of the estimates are strongly related to the compliance of the model assumption with the empirical distribution. In order to investigate the individual pure premium, we introduce an alternative pricing model based on Quantile Regression, to perform a working classification ratemaking with weaker assumptions and, then, more performing for risk margin evaluation.

Keywords: Quantile regression; Generalized linear model; Premium principles; Risk margin; Ratemaking (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_15

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DOI: 10.1007/978-3-319-89824-7_15

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