Estimation of value-at-risk by \(L^p\) quantile regression

DOI10.1007/S10463-024-00911-YMaRDI QIDQ6664136

Hai-yang Xu, Kaizhi Yu, Fuming Lin, Peng Sun

Publication date: 16 January 2025

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

zbMATH Keywords

GARCH models\(L^p\) quantile regressioncalculation of VaRCAR-\(L^p\)-quantile modelsCLVaR models

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Asymmetric Least Squares Estimation and Testing
• Assessing value at risk with CARE, the conditional autoregressive expectile models
• Conditional \(L_ p\)-quantiles and their application to the testing of symmetry in non-parametric regression
• Nonlinear expectile regression with application to value-at-risk and expected shortfall estimation
• Extreme M-quantiles as risk measures: from \(L^{1}\) to \(L^{p}\) optimization
• Relating quantiles and expectiles under weighted-symmetry
• The \(k\)th power expectile regression
• Coherent measures of risk
• Coherence and elicitability
• M-quantiles
• Regression Quantiles
• Penalized high‐dimensional M‐quantile regression: From L¹ to L^p optimization
• The \(k\)th power expectile estimation and testing

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