A hybrid importance sampling algorithm for value-at-risk

Tian Shyr Dai*, Shih Kuei Lin, Li Min Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Value-at-Risk (VaR) provides a number that measures the risk of a financial portfolio under significant loss. Glasserman et al. suggest that the performance of Mote Calo simulation can be improved by importance sampling [3]. However, their technique might perform poorly for some complex portfolios like shorting straddle options. In this paper, we investigate the hybrid importance sampling algorithm which can efficiently estimate the VaR for complex portfolios.

Original languageEnglish
Title of host publicationSecond International Conference on Innovative Computing, Information and Control, ICICIC 2007
PublisherIEEE Computer Society
ISBN (Print)0769528821, 9780769528823
DOIs
StatePublished - 1 Jan 2007
Event2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007 - Kumamoto, Japan
Duration: 5 Sep 20077 Sep 2007

Publication series

NameSecond International Conference on Innovative Computing, Information and Control, ICICIC 2007

Conference

Conference2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007
CountryJapan
CityKumamoto
Period5/09/077/09/07

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