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• W2963491750 abstract "Abstract Due to their heterogeneity, insurance risks can be properly described as a mixture of different fixed models, where the weights assigned to each model may be estimated empirically from a sample of available data. If a risk measure is evaluated on the estimated mixture instead of the (unknown) true one, then it is important to investigate the committed error. In this paper we study the asymptotic behaviour of estimated risk measures, as the data sample size tends to infinity, in the fashion of large deviations. We obtain large deviation results by applying the contraction principle, and the rate functions are given by a suitable variational formula; explicit expressions are available for mixtures of two models. Finally, our results are applied to the most common risk measures, namely the quantiles, the Expected Shortfall and the shortfall risk measure." @default.
• W2963491750 created "2019-07-30" @default.
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• W2963491750 date "2018-05-01" @default.
• W2963491750 modified "2023-09-30" @default.
• W2963491750 title "Large deviations for risk measures in finite mixture models" @default.
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• W2963491750 doi "https://doi.org/10.1016/j.insmatheco.2018.03.005" @default.
• W2963491750 hasPublicationYear "2018" @default.
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