Quantifying and Correcting the Bias in Estimated Risk Measures

DOI10.2143/AST.37.2.2024072zbMath1154.62378OpenAlexW4249401458MaRDI QIDQ5505905

Joseph H. T. Kim, Mary R. Hardy

Publication date: 28 January 2009

Published in: ASTIN Bulletin (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2143/ast.37.2.2024072

zbMATH Keywords

tablessimulations

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Point estimation (62F10) Bootstrap, jackknife and other resampling methods (62F40) Monte Carlo methods (65C05)

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Cites Work

• VaR is subject to a significant positive bias
• A new distribution-free quantile estimator
• The Exact Bootstrap Mean and Variance of an L-estimator
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