Composite bias-reduced Lp−quantile-based estimators of extreme quantiles and expectiles

Quantiles are a fundamental concept in extreme value theory. They can be obtained from a minimization framework using an absolute error loss criterion. The companion notion of expectiles, based on squared rather than absolute error loss minimization, has received substantial attention from the fields of actuarial science, finance and econometrics over the last decade. Quantiles and expectiles can be embedded in a common framework of Lp−quantiles, whose extreme value properties have been explored very recently. Although this generalized notion of quantiles has shown potential for the estimation of extreme quantiles and expectiles, available estimators remain quite difficult to use: they suffer from substantial bias and the question of the choice of the tuning parameter p remains open. In this paper, we work in a context of heavy tails, and we construct composite bias-reduced estimators of extreme quantiles and expectiles based on Lp−quantiles. We provide a discussion of the data-driven choice of p and of the anchor Lp−quantile level in practice. The proposed methodology is compared to existing approaches on simulated data and real data