Show simple item record

A Large Deviations Analysis of Quantile Estimation with Application to Value at Risk

dc.contributor.authorJin, Xing
dc.contributor.authorFu, Michael C.
dc.description.abstractQuantile estimation has become increasingly important, particularly in the financial industry, where Value-at-Risk has emerged as a standard measurement tool for controlling portfolio risk. In this paper we apply the theory of large deviations to analyze various simulation-based quantile estimators. First, we show that the coverage probability of the standard quantile estimator converges to one exponentially fast with sample size. Then we introduce a new quantile estimator that has a provably faster convergence rate. Furthermore, we show that the coverage probability for this new estimator can be guaranteed to be 100% with sufficiently large, but finite, sample size. Numerical experiments on a VaR example illustrate the potential for dramatic variance reduction.en
dc.format.extent364407 bytes
dc.subjectquantile estimationen
dc.subjectlarge deviationsen
dc.subjectvalue at risken
dc.titleA Large Deviations Analysis of Quantile Estimation with Application to Value at Risken
dc.relation.isAvailableAtRobert H. Smith School of Businessen_us
dc.relation.isAvailableAtDecision & Information Technologiesen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_us

Files in this item


This item appears in the following Collection(s)

Show simple item record