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

^*此作品的通讯作者

经济管理学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
页	312-316
页数	5
DOI	https://doi.org/10.1109/CSIE.2009.192
出版状态	已出版 - 2009
活动	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 - Los Angeles, CA, 美国期限: 31 3月 2009 → 2 4月 2009

出版系列

姓名	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
卷	2

会议

会议	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
国家/地区	美国
市	Los Angeles, CA
时期	31/03/09 → 2/04/09

访问文件

10.1109/CSIE.2009.192

其它文件与链接

链接到 Scopus 的出版物

引用此

@inproceedings{dc869979a93f42fc92ed319c16b1bcae,

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. 在 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, 卷 2, 页码 312-316, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, Los Angeles, CA, 美国, 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. 页码 312-316 5171350 (2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009; 卷 2).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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AU - Cui, Wang

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N2 - 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.

AB - 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.

KW - Low-discrepancy sequences

KW - Memory reduction

KW - Multi-asset American options

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DO - 10.1109/CSIE.2009.192

M3 - 会议稿件

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BT - 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009

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Y2 - 31 March 2009 through 2 April 2009

ER -

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

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