Efficiency Improvements for Pricing American Options with a Stochastic Mesh

Other Titles
Abstract
rlo 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.
Journal / Series
Volume & Issue
Description
Sponsorship
Date Issued
2003-01-23
Publisher
Cornell University
Keywords
theory center
Location
Effective Date
Expiration Date
Sector
Employer
Union
Union Local
NAICS
Number of Workers
Committee Chair
Committee Co-Chair
Committee Member
Degree Discipline
Degree Name
Degree Level
Related Version
Related DOI
Related To
Related Part
Based on Related Item
Has Other Format(s)
Part of Related Item
Related To
Related Publication(s)
Link(s) to Related Publication(s)
References
Link(s) to Reference(s)
Previously Published As
http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.tc/2003-287
Government Document
ISBN
ISMN
ISSN
Other Identifiers
Rights
Rights URI
Types
technical report
Accessibility Feature
Accessibility Hazard
Accessibility Summary
Link(s) to Catalog Record