Learning parameter dependence for Fourier-based option pricing with tensor trains

• Item type: SIG Technical Reports(1)
• 公開日: 2024-10-21
• タイトル: Learning parameter dependence for Fourier-based option pricing with tensor trains
• タイトル / 言語: en
• タイトル: Learning parameter dependence for Fourier-based option pricing with tensor trains
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Department of Physics, The University of Tokyo／Department of Physics, Saitama University
• 著者所属: Department of Physics, Saitama University
• 著者所属: Center for Quantum Information and Quantum Biology, Osaka University
• 著者所属(英): en
• 著者所属(英): Department of Physics, The University of Tokyo / Department of Physics, Saitama University
• 著者所属(英): en
• 著者所属(英): Department of Physics, Saitama University
• 著者所属(英): en
• 著者所属(英): Center for Quantum Information and Quantum Biology, Osaka University
• 著者名: Rihito, Sakurai; Haruto, Takahashi; Koichi, Miyamoto
• 著者名(英): Rihito, Sakurai; Haruto, Takahashi; Koichi, Miyamoto
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12894105
• 書誌情報: 研究報告量子ソフトウェア（QS）; 巻 2024-QS-13, 号 4, p. 1-11, 発行日 2024-10-21
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2435-6492
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会