Bias Reduction for Pricing American Options by Least-Squares Monte Carlo
We derive an approximation to the bias in regression-based Monte Carlo estimators of American option values. This derivation holds for general asset-price processes of any dimensionality and for general pay-off structures. It uses the large sample properties of least-squares regression estimators. Bias-corrected estimators result by subtracting the bias approximation from the uncorrected estimator at each exercise opportunity. Numerical results show that the bias-corrected estimator outperforms its uncorrected counterpart across all combinations of number of exercise opportunities, option moneyness and sample size. Finally, the results suggest significant computational efficiency increases can be realized through trivial parallel implementations using the corrected estimator.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 19 (2012)
Issue (Month): 3 (July)
|Contact details of provider:|| Web page: http://www.tandfonline.com/RAMF20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/RAMF20|
When requesting a correction, please mention this item's handle: RePEc:taf:apmtfi:v:19:y:2012:i:3:p:195-217. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If references are entirely missing, you can add them using this form.