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Efficient Parallel Implementation of a Weather Derivatives Pricing Algorithm based on the Fast Gauss Transform
http://hdl.handle.net/2237/9478
http://hdl.handle.net/2237/9478c5b19ddf-a089-418c-916d-d98648238e72
名前 / ファイル | ライセンス | アクション |
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yamamoto_1.pdf (1.1 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2008-02-26 | |||||
タイトル | ||||||
タイトル | Efficient Parallel Implementation of a Weather Derivatives Pricing Algorithm based on the Fast Gauss Transform | |||||
言語 | en | |||||
著者 |
Yamamoto, Yusaku
× Yamamoto, Yusaku |
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アクセス権 | ||||||
アクセス権 | open access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
権利 | ||||||
言語 | en | |||||
権利情報 | Copyright © 2006 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | CDD weather derivatives are widely used to hedge weather risks and their fast and accurate pricing is an important problem in financial engineering. In this paper, we propose an efficient parallelization strategy of a pricing algorithm for the CDD derivatives. The algorithm uses the fast Gauss transform to compute the expected payoff of the derivative and has proved faster and more accurate than the conventional Monte Carlo method. However, speeding up the algorithm on a distributed-memory parallel computer is not straightforward because naїve parallelization will require a large amount of inter-processor communication. our new parallelization strategy exploits the structure of the fast Gauss transform and thereby reduces the amount of inter-processor communication considerably. Numerical experiments show that our strategy achieves up to 50 % performance improvement over the naїve one on an 16-node Mac G5 cluster and can compute the price of a representative CDD derivative in 7 seconds. This speed is adequate for almost any applications. | |||||
言語 | en | |||||
出版者 | ||||||
出版者 | IEEE | |||||
言語 | en | |||||
言語 | ||||||
言語 | eng | |||||
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資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
出版タイプ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1109/IPDPS.2006.1639615 | |||||
ISBN | ||||||
関連タイプ | isPartOf | |||||
識別子タイプ | ISBN | |||||
関連識別子 | 1-4244-0054-6 | |||||
書誌情報 |
en : 20th International Parallel and Distributed Processing Symposium p. 8-8, 発行日 2006 |
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値 | application/pdf | |||||
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値 | publisher | |||||
URI | ||||||
識別子 | http://hdl.handle.net/2237/9478 | |||||
識別子タイプ | HDL |