Publication: Estimation of the ex ante Distribution of Returns for a Portfolio of U.S. Treasury Securities via Deep Learning
dc.contributor.author | Foresti, Andrea | |
dc.date.accessioned | 2019-03-27T14:54:07Z | |
dc.date.available | 2019-03-27T14:54:07Z | |
dc.date.issued | 2019-03 | |
dc.description.abstract | This paper presents different deep neural network architectures designed to forecast the distribution of returns on a portfolio of U.S. Treasury securities. A long short-term memory model and a convolutional neural network are tested as the main building blocks of each architecture. The models are then augmented by cross-sectional data and the portfolio's empirical distribution. The paper also presents the fit and generalization potential of each approach. | en |
dc.identifier | http://documents.worldbank.org/curated/en/433791553192242300/Estimation-of-the-ex-ante-Distribution-of-Returns-for-a-Portfolio-of-U-S-Treasury-Securities-via-Deep-Learning | |
dc.identifier.doi | 10.1596/1813-9450-8790 | |
dc.identifier.uri | https://hdl.handle.net/10986/31449 | |
dc.language | English | |
dc.publisher | World Bank, Washington, DC | |
dc.relation.ispartofseries | Policy Research Working Paper;No. 8790 | |
dc.rights | CC BY 3.0 IGO | |
dc.rights.holder | World Bank | |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/igo | |
dc.subject | MACHINE LEARNING | |
dc.subject | NEURAL NETWORKS | |
dc.subject | CONVOLUTION | |
dc.subject | LSTM | |
dc.subject | MARKET RISK | |
dc.subject | SECURITIES PORTFOLIO | |
dc.title | Estimation of the ex ante Distribution of Returns for a Portfolio of U.S. Treasury Securities via Deep Learning | en |
dc.type | Working Paper | en |
dc.type | Document de travail | fr |
dc.type | Documento de trabajo | es |
dspace.entity.type | Publication | |
okr.crossref.title | Estimation of the ex ante Distribution of Returns for a Portfolio of U.S. Treasury Securities via Deep Learning | |
okr.date.disclosure | 2019-03-21 | |
okr.doctype | Publications & Research | |
okr.doctype | Publications & Research::Policy Research Working Paper | |
okr.docurl | http://documents.worldbank.org/curated/en/433791553192242300/Estimation-of-the-ex-ante-Distribution-of-Returns-for-a-Portfolio-of-U-S-Treasury-Securities-via-Deep-Learning | |
okr.guid | 433791553192242300 | |
okr.identifier.doi | 10.1596/1813-9450-8790 | |
okr.identifier.doi | https://doi.org/10.1596/1813-9450-8790 | |
okr.identifier.externaldocumentum | 090224b086ac2842_2_0 | |
okr.identifier.internaldocumentum | 30921728 | |
okr.identifier.report | WPS8790 | |
okr.imported | true | en |
okr.language.supported | en | |
okr.pdfurl | http://documents.worldbank.org/curated/en/433791553192242300/pdf/WPS8790.pdf | en |
okr.region.country | United States | |
okr.statistics.combined | 1047 | |
okr.statistics.dr | 433791553192242300 | |
okr.statistics.drstats | 825 | |
okr.topic | Macroeconomics and Economic Growth::Economic Theory & Research | |
okr.topic | Science and Technology Development::Science Mathematics and Technology | |
okr.unit | Market and Counterparty Risk Team | |
relation.isSeriesOfPublication | 26e071dc-b0bf-409c-b982-df2970295c87 | |
relation.isSeriesOfPublication.latestForDiscovery | 26e071dc-b0bf-409c-b982-df2970295c87 |
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