About: Pricing foreseeable and unforeseeable risks in insurance portfolios

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Pricing foreseeable and unforeseeable risks in insurance portfolios
Creator	Constantinescu, Corina Egídio Dos Reis, Alfredo Maume-Deschamps, Véronique Ni, Weihong
source	ArXiv
abstract	In this manuscript we propose a method for pricing insurance products that cover not only traditional risks, but also unforeseen ones. By considering the Poisson process parameter to be a mixed random variable, we capture the heterogeneity of foreseeable and unforeseeable risks. To illustrate, we estimate the weights for the two risk streams for a real dataset from a Portuguese insurer. To calculate the premium, we set the frequency and severity as distributions that belong to the linear exponential family. Under a Bayesian setup , we show that when working with a finite mixture of conjugate priors, the premium can be estimated by a mixture of posterior means, with updated parameters, depending on claim histories. We emphasise the riskiness of the unforeseeable trend, by choosing heavy-tailed distributions. After estimating distribution parameters involved using the Expectation-Maximization algorithm, we found that Bayesian premiums derived are more reactive to claim trends than traditional ones.
has issue date	2020-07-15(xsd:dateTime)
has license	arxiv
sha1sum (hex)	8b47616b03d6bb9f1d79944e438e14fb2cea15d5
resource representing a document's title	Pricing foreseeable and unforeseeable risks in insurance portfolios
resource representing a document's body	covid:8b47616b03d6bb9f1d79944e438e14fb2cea15d5#body_text
is schema:about of	named entity 'RISKS' named entity 'INSURANCE' named entity 'Poisson process' named entity 'insurance' named entity 'Gamma random variables' »more»

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