Distortions of multivariate risk measures: a level-sets based approach

Elena Di Bernardino () and Didier Rulliere ()
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Elena Di Bernardino: CEDRIC - Centre d'études et de recherche en informatique et communications - ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise - CNAM - Conservatoire National des Arts et Métiers [CNAM]

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Abstract: In this paper, we propose a parametric model for multivariate distributions. The model is based on distortion functions, i.e. some transformations of a multivariate distribution which permit to generate new families of multivariate distribution functions. We derive some properties of considered distortions. A suitable proximity indicator between level curves is introduced in order to evaluate the quality of candidate distortion parameters. Using this proximity indicator and properties of distorted level curves, we give a specific estimation procedure. The estimation algorithm is mainly relying on straightforward univariate optimizations, and we finally get parametric representations of both multivariate distribution functions and associated level curves. Our results are motivated by applications in multivariate risk theory. The methodology is illustrated on real examples.

Keywords: Multivariate risk measures; Multivariate probability distortions; Level sets estimation; Iterated compositions; Hyperbolic conversion functions; Multivariate risk measures. (search for similar items in EconPapers)
Date: 2012-11-12
Note: View the original document on HAL open archive server: https://hal.science/hal-00756387v1
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