TY - GEN

T1 - A Tutorial on Quantile Estimation via Monte Carlo

AU - Dong, Hui

AU - Nakayama, Marvin K.

N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Confidence intervals

KW - Percentile

KW - Value-at-risk

KW - Variance-reduction techniques

UR - http://www.scopus.com/inward/record.url?scp=85089427060&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85089427060&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-43465-6_1

DO - 10.1007/978-3-030-43465-6_1

M3 - Conference contribution

AN - SCOPUS:85089427060

SN - 9783030434649

T3 - Springer Proceedings in Mathematics and Statistics

SP - 3

EP - 30

BT - Monte Carlo and Quasi-Monte Carlo Methods, MCQMC 2018

A2 - Tuffin, Bruno

A2 - L’Ecuyer, Pierre

PB - Springer

T2 - 13th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2018

Y2 - 1 July 2018 through 6 July 2018

ER -