Non-parametric Estimation of Extreme Risk Measures from Conditional Heavy-tailed Distributions

DOI10.1111/sjos.12078zbMath1305.62199OpenAlexW1997935816MaRDI QIDQ2932770

Laurent Gardes, Stéphane Girard, Jonathan El Methni

Publication date: 9 December 2014

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/sjos.12078

zbMATH Keywords

asymptotic normalityheavy-tailed distributionskernel estimatorrisk measuresextreme-value statisticsconditional tail expectation

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to environmental and related topics (62P12) Nonparametric estimation (62G05) Statistics of extreme values; tail inference (62G32)

Related Items (18)

On the estimation of the functional Weibull tail-coefficient ⋮ Extreme value inference for quantile regression with varying coefficients ⋮ Extreme value estimation of the conditional risk premium in reinsurance ⋮ How a probabilistic analogue of the mean value theorem yields stein-type covariance identities ⋮ Asymptotic results on marginal expected shortfalls for dependent risks ⋮ Extremal quantile autoregression for heavy-tailed time series ⋮ Kernel estimation of extreme regression risk measures ⋮ Asymptotic results on tail moment and tail central moment for dependent risks ⋮ Extreme M-quantiles as risk measures: from \(L^{1}\) to \(L^{p}\) optimization ⋮ ExpectHill estimation, extreme risk and heavy tails ⋮ Tail variance of portfolio under generalized Laplace distribution ⋮ Additive models for extremal quantile regression with Pareto-type distributions ⋮ Unnamed Item ⋮ Tail expectile process and risk assessment ⋮ On a relationship between randomly and non-randomly thresholded empirical average excesses for heavy tails ⋮ Nonparametric estimation of conditional marginal excess moments ⋮ Extreme partial least-squares ⋮ Extreme $$L^p$$-quantile Kernel Regression

Uses Software

• QRM

Cites Work

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