{"created":"2021-03-01T07:02:45.788044+00:00","id":42823,"links":{},"metadata":{"_buckets":{"deposit":"13563980-d825-4999-9f07-ace3c7f59961"},"_deposit":{"id":"42823","owners":[],"pid":{"revision_id":0,"type":"depid","value":"42823"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00042823","sets":["62:7433:7434","9:7435:7436"]},"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2011-03","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"CIRJE-F-791","bibliographic_titles":[{"bibliographic_title":"Discussion paper series. CIRJE-F"}]}]},"item_8_description_13":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Extreme values are often correlated over time, for example, in a financial time series, and these values carry various risks. Max-stable processes such as maxima of moving maxima (M3) processes have been recently considered in the literature to describe timedependent dynamics, which have been difficult to estimate. This paper first proposes a feasible and efficient Bayesian estimation method for nonlinear and non-Gaussian state space models based on these processes and describes a Markov chain Monte Carlo algorithm where the sampling efficiency is improved by the normal mixture sampler. Furthermore, a unique particle filter that adapts to extreme observations is proposed and shown to be highly accurate in comparison with other well-known filters. Our proposed algorithms were applied to daily minima of high-frequency stock return data, and a model comparison was conducted using marginal likelihoods to investigate the time-dependent dynamics in extreme stock returns for financial risk management.","subitem_description_type":"Abstract"}]},"item_8_description_6":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"subsequenlty published in Journal of Time Series Analysis, 33-1, 61-80. January 2012.","subitem_description_type":"Other"},{"subitem_description":"本文フィルはリンク先を参照のこと","subitem_description_type":"Other"}]},"item_8_publisher_20":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"日本経済国際共同センター"}]},"item_8_relation_25":{"attribute_name":"関係URI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://www.cirje.e.u-tokyo.ac.jp/research/dp/2011/2011cf791ab.html","subitem_relation_type_select":"URI"}}]},"item_8_relation_28":{"attribute_name":"置換する","attribute_value_mlt":[{"subitem_relation_type":"replaces","subitem_relation_type_id":{"subitem_relation_type_id_text":"http://hdl.handle.net/2261/37281","subitem_relation_type_select":"URI"}}]},"item_8_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11450569","subitem_source_identifier_type":"NCID"}]},"item_8_subject_15":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"335","subitem_subject_scheme":"NDC"}]},"item_8_text_21":{"attribute_name":"出版者別名","attribute_value_mlt":[{"subitem_text_value":"Center for International Research on the Japanese Economy"}]},"item_8_text_4":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Department of Statistical Science, Duke University"},{"subitem_text_value":"Faculty of Economics, University of Tokyo"},{"subitem_text_value":"Department of Statistics, University of Wisconsin Madison"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kunihama, Tsuyoshi"}],"nameIdentifiers":[{"nameIdentifier":"98455","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Omori, Yasuhiro"}],"nameIdentifiers":[{"nameIdentifier":"98456","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Zhang, Zhengjun"}],"nameIdentifiers":[{"nameIdentifier":"98457","nameIdentifierScheme":"WEKO"}]}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Bayesian analysis","subitem_subject_scheme":"Other"},{"subitem_subject":"Extreme value theory","subitem_subject_scheme":"Other"},{"subitem_subject":"Markov chain Monte Carlo","subitem_subject_scheme":"Other"},{"subitem_subject":"Marginal likelihood","subitem_subject_scheme":"Other"},{"subitem_subject":"Maxima of moving maxima processes","subitem_subject_scheme":"Other"},{"subitem_subject":"Stock returns","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"technical report","resourceuri":"http://purl.org/coar/resource_type/c_18gh"}]},"item_title":"Efficient Estimation and Particle Filter for Max-Stable Processes","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Efficient Estimation and Particle Filter for Max-Stable Processes"}]},"item_type_id":"8","owner":"1","path":["7436","7434"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-01-17"},"publish_date":"2017-01-17","publish_status":"0","recid":"42823","relation_version_is_last":true,"title":["Efficient Estimation and Particle Filter for Max-Stable Processes"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T04:17:59.533431+00:00"}