# A New Algorithm for Monte Carlo for American Options

Mallier, Roland; Alobaidi, Ghada

Serdica Mathematical Journal (2003)

- Volume: 29, Issue: 3, page 271-290
- ISSN: 1310-6600

## Access Full Article

top## Abstract

top## How to cite

topMallier, Roland, and Alobaidi, Ghada. "A New Algorithm for Monte Carlo for American Options." Serdica Mathematical Journal 29.3 (2003): 271-290. <http://eudml.org/doc/219630>.

@article{Mallier2003,

abstract = {2000 Mathematics Subject Classification: 91B28, 65C05.We consider the valuation of American options using Monte
Carlo simulation, and propose a new technique which involves approximating
the optimal exercise boundary. Our method involves splitting the boundary
into a linear term and a Fourier series and using stochastic optimization in
the form of a relaxation method to calculate the coefficients in the series.
The cost function used is the expected value of the option using the the
current estimate of the location of the boundary. We present some sample
results and compare our results to other methods.},

author = {Mallier, Roland, Alobaidi, Ghada},

journal = {Serdica Mathematical Journal},

keywords = {American Options; Monte Carlo; American options},

language = {eng},

number = {3},

pages = {271-290},

publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},

title = {A New Algorithm for Monte Carlo for American Options},

url = {http://eudml.org/doc/219630},

volume = {29},

year = {2003},

}

TY - JOUR

AU - Mallier, Roland

AU - Alobaidi, Ghada

TI - A New Algorithm for Monte Carlo for American Options

JO - Serdica Mathematical Journal

PY - 2003

PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences

VL - 29

IS - 3

SP - 271

EP - 290

AB - 2000 Mathematics Subject Classification: 91B28, 65C05.We consider the valuation of American options using Monte
Carlo simulation, and propose a new technique which involves approximating
the optimal exercise boundary. Our method involves splitting the boundary
into a linear term and a Fourier series and using stochastic optimization in
the form of a relaxation method to calculate the coefficients in the series.
The cost function used is the expected value of the option using the the
current estimate of the location of the boundary. We present some sample
results and compare our results to other methods.

LA - eng

KW - American Options; Monte Carlo; American options

UR - http://eudml.org/doc/219630

ER -

## NotesEmbed ?

topTo embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.