Asymptotic representations for importance-sampling estimators of value-at-risk and conditional value-at-risk

DOI10.1016/j.orl.2010.02.007zbMath1193.91165OpenAlexW1979348268MaRDI QIDQ991456

L. Jeff Hong, Lihua Sun

Publication date: 7 September 2010

Published in: Operations Research Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.orl.2010.02.007

zbMATH Keywords

importance samplingasymptotic representationvalue-at-riskconditional value-at-risk

Mathematics Subject Classification ID

Related Items (9)

Black-Litterman model for continuous distributions ⋮ The Convergence Rate and Asymptotic Distribution of the Bootstrap Quantile Variance Estimator for Importance Sampling ⋮ Variance reduction for risk measures with importance sampling in nested simulation ⋮ Simulating risk measures via asymptotic expansions for relative errors ⋮ A Tutorial on Quantile Estimation via Monte Carlo ⋮ Convergence analysis of quasi-Monte Carlo sampling for quantile and expected shortfall ⋮ Confidence Intervals for Quantiles Using Sectioning When Applying Variance-Reduction Techniques ⋮ Concentration bounds for empirical conditional value-at-risk: the unbounded case ⋮ Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk

Cites Work

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• On the subspaces of \(L^p\) \((p > 2)\) spanned by sequences of independent random variables
• Variance Reduction Techniques for Estimating Value-at-Risk
• Importance Sampling for Portfolio Credit Risk
• Simulating Sensitivities of Conditional Value at Risk
• Portfolio Value-at-Risk with Heavy-Tailed Risk Factors
• Uniformly Efficient Importance Sampling for the Tail Distribution of Sums of Random Variables
• A Note on Quantiles in Large Samples

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