{"created":"2023-05-15T11:51:38.728766+00:00","id":13683,"links":{},"metadata":{"_buckets":{"deposit":"e878f7ba-2058-45c3-b06e-3336d9326030"},"_deposit":{"created_by":10,"id":"13683","owners":[10],"pid":{"revision_id":0,"type":"depid","value":"13683"},"status":"published"},"_oai":{"id":"oai:kanagawa-u.repo.nii.ac.jp:00013683","sets":["308:309:310:1323"]},"author_link":["35789","35789"],"item_3_alternative_title_20":{"attribute_name":"その他の言語のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"An AI Application To Derivatives Pricing And Its Prospects"}]},"item_3_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-25","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"47","bibliographicPageEnd":"37","bibliographicPageStart":"23","bibliographic_titles":[{"bibliographic_title":"経済貿易研究 : 研究所年報","bibliographic_titleLang":"ja"},{"bibliographic_title":"The Studies in Economics and Trade","bibliographic_titleLang":"en"}]}]},"item_3_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"金融実務において、派生証券（デリバティブ）の価格付けには（ 1 ）モンテカルロ法（MC）や有限差分法（FDM）などの数値計算法、（ 2 ）特異摂動法や漸近展開法などの近似手法が用いられている。一方で、ここ数年のファイナンスにおける機械学習（Machine learning（ML））の発展は目覚ましく、数多くの手法が提案されており、数値計算法や近似手法に取って変わる勢いである。本稿では、派生証券（デリバティブ）の価値評価に関する問題に対して、Funahashi（2021）で提案された人工回路網（Artificial neural network, (ANN)）を活用したアプローチを中心に紹介し、SABR モデルを用いて実務への応用例を示しながら、その有用性について議論する。","subitem_description_type":"Abstract"},{"subitem_description":"In financial practice, ( 1 ) numerical methods, such as Monte Carlo simulation (MC) or finite difference method (FDM), and ( 2 ) approximation methods, such as asymptotic expansion or singular perturbation techniques, have been widely used. In contrast, recent progress in machine learning (ML) in the field of finance has shown remarkable development. Various methods have been proposed to price derivatives or calibrate financial asset-pricing models using the artificial neural network (ANN), which seems to be taking the place of the numerical and approximation methods. This note provides an ANN application to the SABR model and discusses the prospects for the ANN application in financial problems.","subitem_description_type":"Abstract"}]},"item_3_description_40":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"subitem_description":"Departmental Bulletin Paper","subitem_description_type":"Other"}]},"item_3_description_5":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"論説","subitem_description_type":"Other"}]},"item_3_publisher_33":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"神奈川大学経済貿易研究所"},{"subitem_publisher":"Kanagawa University The Institute of Economics and Trade"}]},"item_3_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00071389","subitem_source_identifier_type":"NCID"}]},"item_3_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0386-5193","subitem_source_identifier_type":"ISSN"}]},"item_3_version_type_16":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"舟橋, 秀治"}],"nameIdentifiers":[{"nameIdentifier":"35789","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"35789","nameIdentifierScheme":"ローカル著者ID","nameIdentifierURI":" "}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2021-04-01"}],"displaytype":"detail","filename":"02 派生証券の価格評価における人工知能の活用とその展望.pdf","filesize":[{"value":"23.1 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"02 派生証券の価格評価における人工知能の活用とその展望","url":"https://kanagawa-u.repo.nii.ac.jp/record/13683/files/02 派生証券の価格評価における人工知能の活用とその展望.pdf"},"version_id":"72350d01-a0b1-4de7-9b13-5a13572dac56"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工回路網","subitem_subject_scheme":"Other"},{"subitem_subject":"ディープ・ラーニング","subitem_subject_scheme":"Other"},{"subitem_subject":"機械学習","subitem_subject_scheme":"Other"},{"subitem_subject":"近似解","subitem_subject_scheme":"Other"},{"subitem_subject":"オプション","subitem_subject_scheme":"Other"},{"subitem_subject":"モンテカルロ法","subitem_subject_scheme":"Other"},{"subitem_subject":"SABR モデル","subitem_subject_scheme":"Other"},{"subitem_subject":"Artificial neural network","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"deep learning machine learing","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"approxination mefhod","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"option pricing","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"monte calro method","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"SABR model","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"派生証券の価格評価における人工知能の活用とその展望","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"派生証券の価格評価における人工知能の活用とその展望","subitem_title_language":"ja"},{"subitem_title":"An AI Application To Derivatives Pricing And Its Prospects","subitem_title_language":"en"}]},"item_type_id":"3","owner":"10","path":["1323"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2021-04-01"},"publish_date":"2021-04-01","publish_status":"0","recid":"13683","relation_version_is_last":true,"title":["派生証券の価格評価における人工知能の活用とその展望"],"weko_creator_id":"10","weko_shared_id":-1},"updated":"2023-06-20T04:39:55.813019+00:00"}