Estimation of tail risk based on extreme expectiles

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INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles (2018, background): “Data were not being actively recorded at many of the stations during the training period, resulting in a mixed dataset comprised of a few time-rich stations and many time-poor ones; the available sample size varies from n = 0 at Stations 6–10 and 37, to n = 8387 at Station 39; see Fig. 1 and Wintenb”
Extreme M-quantiles as risk measures: From $L^{1}$ to $L^{p}$ optimization (2019, methodology): “In order to decide on the global accuracy of the three competing methods, we shall need to rank the values of their realized losses L̄ pmq n by making use of the T forecasts and realizing observations, as described in (18).”