# Extreme M-quantiles as risk measures: From L1 to Lp optimization

2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : The class of quantiles lies at the heart of extreme-value theory and is one of the basic tools in risk management. The alternative family of expectiles is based on squared rather than absolute error loss minimization. It has recently been receiving a lot of attention in actuarial science, econometrics and statistical finance. Both quantiles and expectiles can be embedded in a more general class of M-quantiles by means of Lp optimization. These generalized Lp quantiles steer an advantageous middle course between ordinary quantiles and expectiles without sacrificing their virtues too much for $p\in[1,2]$. In this paper, we investigate their estimation from the perspective of extreme values in the class of heavy-tailed distributions. We construct estimators of the intermediate Lp quantiles and establish their asymptotic normality in a dependence framework motivated by financial and actuarial applications, before extrapolating these estimates to the very far tails. We also investigate the potential of extreme Lp quantiles as a tool for estimating the usual quantiles and expectiles themselves. We show the usefulness of extreme Lp quantiles and elaborate the choice of p through applications to some simulated and financial real data.
Keywords :
Document type :
Journal articles
Domain :

Cited literature [7 references]

https://hal.inria.fr/hal-01585215
Contributor : Stephane Girard <>
Submitted on : Wednesday, October 9, 2019 - 9:23:09 AM
Last modification on : Thursday, November 7, 2019 - 2:18:02 PM

### Files

DGS2_main_typesetting_final.pd...
Files produced by the author(s)

### Citation

Abdelaati Daouia, Stéphane Girard, Gilles Stupfler. Extreme M-quantiles as risk measures: From L1 to Lp optimization. Bernoulli, Bernoulli Society for Mathematical Statistics and Probability, 2019, 25 (1), pp.264-309. ⟨10.3150/17-BEJ987⟩. ⟨hal-01585215v2⟩

Record views