We use tail expectiles to estimate alternative measures to the value at risk and marginal
expected shortfall, which are two instruments of risk protection of utmost importance in …

Denote the loss return on the equity of a financial institution as X and that of the entire
market as Y. For a given very small value of p> 0, the marginal expected shortfall (MES) is …

Tail expectile process and risk assessment Page 1 Bernoulli 26(1), 2020, 531–556 https://doi.org/10.3150/19-BEJ1137
Tail expectile process and risk assessment ABDELAATI DAOUIA1, STÉPHANE GIRARD2 …

Extreme M-quantiles as risk measures: From L1 to Lp optimization Page 1 Bernoulli 25(1), 2019,
264–309 https://doi.org/10.3150/17-BEJ987 Extreme M-quantiles as risk measures: From L 1 to L …

We propose a neural networks method to estimate extreme Expected Shortfall, and even
more generally, extreme conditional tail moments as functions of confidence levels, in heavy …

Expectiles and quantiles can both be defined as the solution of minimization problems.
Contrary to quantiles though, expectiles are determined by tail expectations rather than tail …

In this paper, we build simple extreme analogues of Wang distortion risk measures and we
show how this makes it possible to consider many standard measures of extreme risk …

Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail
Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and …

This paper explores ways to improve the existing systemic risk measures by incorporating
machine learning algorithms into the measurement. We aim to overcome the shortcomings …

We propose in this article a statistical framework to study local disparities in the co‐
occurrence of extreme rainfall in the French Mediterranean region. We employ a region‐of …