Extreme M-quantiles as risk measures: from \(L^{1}\) to \(L^{p}\) optimization
From MaRDI portal
Publication:1715530
DOI10.3150/17-BEJ987zbMath1442.62106OpenAlexW2760574762MaRDI QIDQ1715530
Stéphane Girard, Gilles Stupfler, Abdelaati Daouia
Publication date: 28 January 2019
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.bj/1544605247
asymptotic normalityextreme valuesextrapolationmixingheavy tailsquantilesexpectilesdependent observationstail risk\(L^{p}\) optimization
Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistics of extreme values; tail inference (62G32)
Related Items (18)
Joint inference on extreme expectiles for multivariate heavy-tailed distributions ⋮ Multivariate \(\rho \)-quantiles: a spatial approach ⋮ On automatic bias reduction for extreme expectile estimation ⋮ Composite bias‐reduced Lp‐quantile‐based estimators of extreme quantiles and expectiles ⋮ Automatic selection by penalized asymmetric L q -norm in a high-dimensional model with grouped variables ⋮ Asymptotic properties of generalized shortfall risk measures for heavy-tailed risks ⋮ A refined Weissman estimator for extreme quantiles ⋮ Estimation of conditional extreme risk measures from heavy-tailed elliptical random vectors ⋮ Extreme M-quantiles as risk measures: from \(L^{1}\) to \(L^{p}\) optimization ⋮ ExpectHill estimation, extreme risk and heavy tails ⋮ Tail expectile process and risk assessment ⋮ On a relationship between randomly and non-randomly thresholded empirical average excesses for heavy tails ⋮ Semi-parametric estimation of multivariate extreme expectiles ⋮ Tail risk inference via expectiles in heavy-tailed time series ⋮ Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models ⋮ On the estimation of the variability in the distribution tail ⋮ The MLE of Aigner, Amemiya, and Poirier is not the expectile MLE ⋮ Extreme $$L^p$$-quantile Kernel Regression
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Making and Evaluating Point Forecasts
- Extreme quantile estimation for dependent data, with applications to finance
- Adapting extreme value statistics to financial time series: dealing with bias and serial dependence
- Asymmetric Least Squares Estimation and Testing
- Assessing value at risk with CARE, the conditional autoregressive expectile models
- A moment estimator for the conditional extreme-value index
- Nonparametric estimation of multivariate extreme-value copulas
- Estimation of the conditional tail index using a smoothed local Hill estimator
- Risk concentration based on expectiles for extreme risks under FGM copula
- Towards estimating extremal serial dependence via the bootstrapped extremogram
- The extremogram: a correlogram for extreme events
- Frontier estimation and extreme value theory
- Second order regular variation and conditional tail expectation of multiple risks
- An introduction to copulas.
- A moment estimator for the index of an extreme-value distribution
- Basic properties of strong mixing conditions. A survey and some open questions
- Limit theorems for empirical processes of cluster functionals
- Inference for the limiting cluster size distribution of extreme values
- Estimating the multivariate extremal index function
- Weak convergence of the tail empirical process for dependent sequences
- On the central limit theorem for \(\rho\)-mixing sequences of random variables
- A central limit theorem for stationary \(\rho\)-mixing sequences with infinite variance
- Conditional \(L_ p\)-quantiles and their application to the testing of symmetry in non-parametric regression
- On tail index estimation using dependent data
- Statistical inference using extreme order statistics
- A simple general approach to inference about the tail of a distribution
- Tail index estimation for dependent data
- Expectiles and \(M\)-quantiles are quantiles
- Nonparametric estimation equations for time series data.
- Extreme M-quantiles as risk measures: from \(L^{1}\) to \(L^{p}\) optimization
- Weighted approximations of tail processes for \(\beta\)-mixing random variables.
- Asymptotic distribution of smoothers based on local means and local medians under dependence
- Relating quantiles and expectiles under weighted-symmetry
- Generalized quantiles as risk measures
- On kernel smoothing for extremal quantile regression
- Generalizing Koenker's distribution
- Measures of serial extremal dependence and their estimation
- Extreme-quantile tracking for financial time series
- Coherent Measures of Risk
- COHERENCE AND ELICITABILITY
- A local moment type estimator for the extreme value index in regression with random covariates
- Non-parametric Estimation of Extreme Risk Measures from Conditional Heavy-tailed Distributions
- Extreme versions of Wang risk measures and their estimation for heavy-tailed distributions
- On the Central Limit Theorem for $\varphi$-Mixing Arrays of Random Variables
- M-quantiles
- A Note on the Central Limit Theorems for Dependent Random Variables
- Regression Quantiles
- Estimation of Parameters and Larger Quantiles Based on the k Largest Observations
- Asymmetric least squares regression estimation: A nonparametric approach∗
- Asymptotic behavior of hill's estimator for autoregressive data
- Estimation of Tail Risk Based on Extreme Expectiles
- Statistics of Extremes
- Consistency of Hill's estimator for dependent data
- Expectile and quantile regression—David and Goliath?
- ASYMPTOTIC EXPANSIONS OF GENERALIZED QUANTILES AND EXPECTILES FOR EXTREME RISKS
- Estimation of the Marginal Expected Shortfall: the Mean When a Related Variable is Extreme
- Estimation of Extreme Quantiles for Functions of Dependent Random Variables
- Heavy-Tail Phenomena
- Nonparametric estimation of a conditional quantile for \(\alpha\)-mixing processes
This page was built for publication: Extreme M-quantiles as risk measures: from \(L^{1}\) to \(L^{p}\) optimization
