Large deviations for risk measures in finite mixture models

Valeria Bignozzi, Claudio Macci and Lea Petrella

Papers from arXiv.org

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 measures.

Date: 2017-10, Revised 2018-02
New Economics Papers: this item is included in nep-cta, nep-ecm and nep-rmg
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Citations: View citations in EconPapers (1)

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Journal Article: Large deviations for risk measures in finite mixture models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1710.03252

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