A Tutorial on Quantile Estimation via Monte Carlo

Document Type

Conference Proceeding

Publication Date

1-1-2020

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.

Identifier

85089427060 (Scopus)

ISBN

[9783030434649]

Publication Title

Springer Proceedings in Mathematics and Statistics

External Full Text Location

https://doi.org/10.1007/978-3-030-43465-6_1

e-ISSN

21941017

ISSN

21941009

First Page

3

Last Page

30

Volume

324

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