A memory reduction Monte Carlo simulation for pricing multi-assets American options

Yang Haijun; Wang Cui

doi:10.1109/CSIE.2009.192

A memory reduction Monte Carlo simulation for pricing multi-assets American options

Yang Haijun^*
, Wang Cui

^*Corresponding author for this work

School of Economics and Management

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

When pricing American options on multi-assets (d) by Monte Carlo methods, one usually stores the simulated asset prices at all time steps on all paths in order to determine when to exercise the options. If N time steps and M paths are used, then the storage requirement is d x M x N . It is undoubtedly enormous for Monte Carlo method which needs to increase the number of simulations to improve the accuracy. In this paper, we propose a memory reduction simulation method to price multi-asset American options and use it in low-discrepancy sequences. For machines with limited memory, we can now use larger values of M and N to improve the accuracy in pricing the options.

Original language	English
Title of host publication	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Pages	312-316
Number of pages	5
DOIs	https://doi.org/10.1109/CSIE.2009.192
State	Published - 2009
Event	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 - Los Angeles, CA, United States Duration: 31 Mar 2009 → 2 Apr 2009

Publication series

Name	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Volume	2

Conference

Conference	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Country/Territory	United States
City	Los Angeles, CA
Period	31/03/09 → 2/04/09

Keywords

Low-discrepancy sequences
Memory reduction
Multi-asset American options

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10.1109/CSIE.2009.192

Cite this

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title = "A memory reduction Monte Carlo simulation for pricing multi-assets American options",

abstract = "When pricing American options on multi-assets (d) by Monte Carlo methods, one usually stores the simulated asset prices at all time steps on all paths in order to determine when to exercise the options. If N time steps and M paths are used, then the storage requirement is d x M x N . It is undoubtedly enormous for Monte Carlo method which needs to increase the number of simulations to improve the accuracy. In this paper, we propose a memory reduction simulation method to price multi-asset American options and use it in low-discrepancy sequences. For machines with limited memory, we can now use larger values of M and N to improve the accuracy in pricing the options.",

keywords = "Low-discrepancy sequences, Memory reduction, Multi-asset American options",

author = "Yang Haijun and Wang Cui",

year = "2009",

doi = "10.1109/CSIE.2009.192",

language = "英语",

isbn = "9780769535074",

series = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009",

pages = "312--316",

booktitle = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009",

note = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 ; Conference date: 31-03-2009 Through 02-04-2009",

}

Haijun, Y & Cui, W 2009, A memory reduction Monte Carlo simulation for pricing multi-assets American options. in 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009., 5171350, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, vol. 2, pp. 312-316, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, Los Angeles, CA, United States, 31/03/09. https://doi.org/10.1109/CSIE.2009.192

A memory reduction Monte Carlo simulation for pricing multi-assets American options. / Haijun, Yang; Cui, Wang.
2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009. 2009. p. 312-316 5171350 (2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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A memory reduction Monte Carlo simulation for pricing multi-assets American options

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