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Efficiency Improvements for Pricing American Options with a Stochastic Mesh

dc.contributor.authorAvramidis, Athanassiosen_US
dc.contributor.authorHyden, Paulen_US
dc.date.accessioned2007-04-02T21:19:07Z
dc.date.available2007-04-02T21:19:07Z
dc.date.issued2003-01-23en_US
dc.description.abstractrlo simulation. First, we develop a mesh-based, biased-low estimator. By recursively averaging the low and high estimators at each stage, we obtain a significantly more accurate point estimator at each of the mesh points. Second, adapt the importance sampling ideas for simulation of European path-dependent options in Glasserman, Heidelberger, and Shahabuddin (1998a) to pricing of American options with a stochastic mesh. Third, we sketch generalizations of the mesh method and we discuss links with other techniques for valuing American options. Our empirical results show that the bias-reduced point estimates are much more accurate than the standard mesh-method point estimators. Importance sampling is found to increase accuracy for a smooth option-payoff functions, while variance increases are possible for non-smooth payoffs.en_US
dc.format.extent135703 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.tc/2003-287en_US
dc.identifier.urihttps://hdl.handle.net/1813/5461
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjecttheory centeren_US
dc.titleEfficiency Improvements for Pricing American Options with a Stochastic Meshen_US
dc.typetechnical reporten_US

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