Kernel estimation for quantile sensitivities

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

2 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - Winter Simulation Conference
Pages941-948
Publication statusPublished - 2007
Externally publishedYes

Publication series

Name
ISSN (Print)0891-7736

Conference

Title2007 Winter Simulation Conference (WSC'07)
PlaceUnited States
CityWashington
Period9 - 12 December 2007

Abstract

Quantiles, also known as value-at-risk in financial applications, are important measures of random performance. Quantile sensitivities provide information on how changes in the input parameters affect the output quantiles. In this paper, we study the estimation of quantile sensitivities using simulation. We propose a new estimator by employing kernel method and show its consistency and asymptotic normality for i.i.d. data. Numerical results show that our estimator works well for the test problems. © 2007 IEEE.

Citation Format(s)

Kernel estimation for quantile sensitivities. / Liu, Guangwu; Hongh, L. Jeff.

Proceedings - Winter Simulation Conference. 2007. p. 941-948 4419690.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review