Bias-reduced and variance-corrected asymptotic Gaussian inference about extreme expectiles

Computational methods for problems pertaining to statistics (62-08) Asymptotic distribution theory in statistics (62E20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistics of extreme values; tail inference (62G32)

Cites Work

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Making and Evaluating Point Forecasts
Asymmetric Least Squares Estimation and Testing
Expectile asymptotics
Heavy-tailed distribution of cyber-risks
Asymptotic properties of the bootstrap for heavy-tailed distributions
Bootstrap of the mean in the infinite variance case
A simple general approach to inference about the tail of a distribution
On the bootstrap of the sample mean in the infinite variance case
Expectiles and \(M\)-quantiles are quantiles
Extreme M-quantiles as risk measures: from \(L^{1}\) to \(L^{p}\) optimization
Generalized quantiles as risk measures
Cyber claim analysis using generalized Pareto regression trees with applications to insurance
Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models
Joint inference on extreme expectiles for multivariate heavy-tailed distributions
On automatic bias reduction for extreme expectile estimation
Tail expectile process and risk assessment
Coherent Measures of Risk
COHERENCE AND ELICITABILITY
Extreme versions of Wang risk measures and their estimation for heavy-tailed distributions
Asymptotic theory for bootstrapping the extremes
Bootstrap confidence intervals for the pareto index
Estimation of Tail Risk Based on Extreme Expectiles
Statistics of Extremes
A PARAMETRIC BOOTSTRAP FOR HEAVY-TAILED DISTRIBUTIONS
Statistical Inference for Expectile‐based Risk Measures
Nonparametric extreme conditional expectile estimation
Composite bias‐reduced Lp‐quantile‐based estimators of extreme quantiles and expectiles