Confidence intervals for quantiles and value-at-risk when applying importance sampling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We develop methods to construct asymptotically valid confidence intervals for quantiles and value-at-risk when applying importance sampling (IS). We first employ IS to estimate the cumulative distribution function (CDF), which we then invert to obtain a point estimate of the quantile. To construct confidence intervals, we show that the IS quantile estimator satisfies a Bahadur-Ghosh representation, which implies a central limit theorem (CLT) for the quantile estimator and can be used to obtain consistent estimators of the variance constant in the CLT.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 Winter Simulation Conference, WSC'10
Pages2751-2761
Number of pages11
DOIs
StatePublished - 2010
Event2010 43rd Winter Simulation Conference, WSC'10 - Baltimore, MD, United States
Duration: Dec 5 2010Dec 8 2010

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Other

Other2010 43rd Winter Simulation Conference, WSC'10
CountryUnited States
CityBaltimore, MD
Period12/5/1012/8/10

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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