Predicting extreme value at risk: nonparametric quantile regression with refinements from extreme value theory

DOI10.1016/j.csda.2012.03.016zbMath1254.91279OpenAlexW2057389198MaRDI QIDQ1927187

Julia Schaumburg

Publication date: 30 December 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2012.03.016

zbMATH Keywords

risk managementmonotonizationvalue at risknonparametric quantile regressionextreme value statistical applications

Mathematics Subject Classification ID

Related Items (11)

Bayesian tail risk interdependence using quantile regression ⋮ Estimation for Extreme Conditional Quantiles of Functional Quantile Regression ⋮ Extreme quantile regression for tail single-index varying-coefficient models ⋮ Semiparametric function-on-function quantile regression model with dynamic single-index interactions ⋮ Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid ⋮ A smooth non-parametric estimation framework for safety-first portfolio optimization ⋮ Improved local quantile regression ⋮ Estimation of High Conditional Quantiles for Heavy-Tailed Distributions ⋮ Two nonparametric approaches to mean absolute deviation portfolio selection model ⋮ Quantile Regression for Location‐Scale Time Series Models with Conditional Heteroscedasticity ⋮ Portfolio optimization by using MeanSharp-βVaR and Multi Objective MeanSharp-βVaR models

Uses Software

• CAViaR
• QRM

Cites Work

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• Extreme quantile estimation for dependent data, with applications to finance
• A simple nonparametric estimator of a strictly monotone regression function
• Nonparametric estimation of conditional VaR and expected shortfall
• A flexible extreme value mixture model
• Estimating tails of probability distributions
• Residual life time at great age
• Statistical inference using extreme order statistics
• A comparison of local constant and local linear regression quantile estimators
• On the use of the peaks over thresholds method for estimating out-of-sample quantiles.
• Improving point and interval estimators of monotone functions by rearrangement
• Local Linear Quantile Regression
• Regression Quantiles
• REGRESSION QUANTILES FOR TIME SERIES

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