{"updated":"2025-01-19T08:00:06.731508+00:00","links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240369","sets":["1164:10193:11470:11768"]},"path":["11768"],"owner":"44499","recid":"240369","title":["Learning parameter dependence for Fourier-based option pricing with tensor trains"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-10-21"},"_buckets":{"deposit":"ae4ae319-f298-4f07-9b96-dde5bbe1165f"},"_deposit":{"id":"240369","pid":{"type":"depid","value":"240369","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Learning parameter dependence for Fourier-based option pricing with tensor trains","author_link":["659281","659279","659278","659280","659277","659276"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Learning parameter dependence for Fourier-based option pricing with tensor trains"},{"subitem_title":"Learning parameter dependence for Fourier-based option pricing with tensor trains","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2024-10-21","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Department of Physics, The University of Tokyo／Department of Physics, Saitama University"},{"subitem_text_value":"Department of Physics, Saitama University"},{"subitem_text_value":"Center for Quantum Information and Quantum Biology, Osaka University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Physics, The University of Tokyo / Department of Physics, Saitama University","subitem_text_language":"en"},{"subitem_text_value":"Department of Physics, Saitama University","subitem_text_language":"en"},{"subitem_text_value":"Center for Quantum Information and Quantum Biology, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/240369/files/IPSJ-QS24013004.pdf","label":"IPSJ-QS24013004.pdf"},"date":[{"dateType":"Available","dateValue":"2026-10-21"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-QS24013004.pdf","filesize":[{"value":"1.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"53"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"da83b2f2-bc8c-4947-a550-8168b40cc585","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Rihito, Sakurai"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Haruto, Takahashi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Koichi, Miyamoto"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Rihito, Sakurai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Haruto, Takahashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Koichi, Miyamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12894105","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2435-6492","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"A long-standing issue in mathematical finance is the speed-up of option pricing, especially for multi-asset options. A recent study has proposed to use tensor train learning algorithms to speed up Fourier transform (FT)-based option pricing, utilizing the ability of tensor trains to compress high-dimensional tensors. Another usage of the tensor train is to compress functions, including their parameter dependence. Here, we propose a pricing method, where, by a tensor train learning algorithm, we build tensor trains that approximate functions appearing in FT-based option pricing with their parameter dependence and efficiently calculate the option price for the varying input parameters. As a benchmark test, we run the proposed method to price a multi-asset option for the various values of volatilities and present asset prices. We show that, in the tested cases involving up to 11 assets, the proposed method outperforms Monte Carlo-based option pricing with 10 5 paths in terms of computational complexity while keeping comparable accuracy.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"A long-standing issue in mathematical finance is the speed-up of option pricing, especially for multi-asset options. A recent study has proposed to use tensor train learning algorithms to speed up Fourier transform (FT)-based option pricing, utilizing the ability of tensor trains to compress high-dimensional tensors. Another usage of the tensor train is to compress functions, including their parameter dependence. Here, we propose a pricing method, where, by a tensor train learning algorithm, we build tensor trains that approximate functions appearing in FT-based option pricing with their parameter dependence and efficiently calculate the option price for the varying input parameters. As a benchmark test, we run the proposed method to price a multi-asset option for the various values of volatilities and present asset prices. We show that, in the tested cases involving up to 11 assets, the proposed method outperforms Monte Carlo-based option pricing with 10 5 paths in terms of computational complexity while keeping comparable accuracy.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"11","bibliographic_titles":[{"bibliographic_title":"研究報告量子ソフトウェア（QS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-10-21","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"2024-QS-13"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":240369,"created":"2025-01-19T01:44:31.558979+00:00"}