Variance reduction for financial options pricing

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

Abstract

Options pricing is still an open area for exact results. Monte Carlo integration is the unique solution for especially complicated options. It is desired to control variability while implementing Monte Carlo techniques. In order to supply reliable results variance of simulation trials should be decreased. Importance Sampling is one of the variance reduction techniques commonly used in Monte Carlo applications. This study includes a research to gather the appropriate Importance Sampling density which gives the lowest variance. We illustrate the Importance Sampling method on financial options and calculate the value of options. By the same way, it is possible to calculate any expectation that cannot be calculated analytically. Numerical results indicate that longer tailed proposal distributions provide substantial decrease in the estimated variance.

Original languageEnglish
Title of host publicationWorld Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf.
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages1190-1195
Number of pages6
ISBN (Print)9789814417730
DOIs
Publication statusPublished - 2012
Event10th International Fuzzy Logic and Intelligent Technologies inNuclear Science Conference, FLINS 2012 - Istanbul, Turkey
Duration: 26 Aug 201229 Aug 2012

Publication series

NameWorld Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf.
Volume7

Conference

Conference10th International Fuzzy Logic and Intelligent Technologies inNuclear Science Conference, FLINS 2012
Country/TerritoryTurkey
CityIstanbul
Period26/08/1229/08/12

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