A general framework of importance sampling for value-at-risk and conditional value-at-risk

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

View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - Winter Simulation Conference
Pages415-422
StatePublished - 2009
Externally publishedYes

Publication series

Name
ISSN (Print)0891-7736

Conference

Title2009 Winter Simulation Conference, WSC 2009
PlaceUnited States
CityAustin
Period13 - 16 December 2009

Abstract

Value-at-risk (VaR) and conditional value-at-risk (CVaR) are important risk measures. Importance sampling (IS) is often used to estimate them. We derive the asymptotic representations for IS estimators of VaR and CVaR. Based on these representations, we are able to give simple conditions under which the IS estimators have smaller asymptotic variances than the ordinal estimators. We show that the exponential twisting can yield an IS distribution that satisfies the conditions for both the IS estimators of VaR and CVaR. Therefore, we may be able to estimate VaR and CVaR accurately at the same time. ©2009 IEEE.

Citation Format(s)

A general framework of importance sampling for value-at-risk and conditional value-at-risk. / Sun, Lihua; Hong, L. Jeff.

Proceedings - Winter Simulation Conference. 2009. p. 415-422 5429348.

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