A Tutorial on Quantile Estimation via Monte Carlo

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1 Scopus citations

Abstract

Quantiles are frequently used to assess risk in a wide spectrum of application areas, such as finance, nuclear engineering, and service industries. This tutorial discusses Monte Carlo simulation methods for estimating a quantile, also known as a percentile or value-at-risk, where p of a distribution’s mass lies below its p-quantile. We describe a general approach that is often followed to construct quantile estimators, and show how it applies when employing naive Monte Carlo or variance-reduction techniques. We review some large-sample properties of quantile estimators. We also describe procedures for building a confidence interval for a quantile, which provides a measure of the sampling error.

Original languageEnglish (US)
Title of host publicationMonte Carlo and Quasi-Monte Carlo Methods, MCQMC 2018
EditorsBruno Tuffin, Pierre L’Ecuyer
PublisherSpringer
Pages3-30
Number of pages28
ISBN (Print)9783030434649
DOIs
StatePublished - 2020
Event13th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2018 - Rennes, France
Duration: Jul 1 2018Jul 6 2018

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume324

Conference

Conference13th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2018
CountryFrance
CityRennes
Period7/1/187/6/18

All Science Journal Classification (ASJC) codes

  • Mathematics(all)

Keywords

  • Confidence intervals
  • Percentile
  • Value-at-risk
  • Variance-reduction techniques

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