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Kernel estimation of extreme risk measures for all domains of attraction

Abstract : Value-at-risk, Conditional Tail Expectation, Conditional Value-at-risk and Conditional Tail Variance are classical risk measures. In statistical terms, the Value-at-risk is the upper α-quantile of the loss distribution where α ∈ (0, 1) is the confidence level. Here, we focus on the properties of these risk measures for extreme losses (where α → 0 is no longer fixed). To assign probabilities to extreme losses we assume that the distribution satisfies a von-Mises condition which allows us to work in the general setting, whether the extreme-value index is positive, negative or zero i.e. for all domains of attraction. We also consider these risk measures in the presence of a covariate. The main goal of this communication is to propose estimators of the above risk measures for all domains of attraction, for extreme losses, and to include a covariate in the estimation. The estimation method thus combines nonparametric kernel methods with extreme-value statistics. The asymptotic distribution of our estimators is established and their finite sam- ple behavior is illustrated on simulated data and on a real data set of daily rainfall.
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https://hal.archives-ouvertes.fr/hal-01062363
Contributor : Jonathan El Methni <>
Submitted on : Tuesday, September 9, 2014 - 4:32:08 PM
Last modification on : Friday, March 27, 2020 - 2:38:29 AM

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  • HAL Id : hal-01062363, version 1

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Jonathan El Methni, Stephane Girard, Laurent Gardes. Kernel estimation of extreme risk measures for all domains of attraction. COMPSTAT 2014 - 21st International Conference on Computational Statistics, Aug 2014, Geneva, Switzerland. pp.CDROM. ⟨hal-01062363⟩

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