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dc.contributor.authorAdam, Timo
dc.contributor.authorOelschläger, Lennart
dc.contributor.editorIrigoien, Itziar
dc.contributor.editorLee, Dae-Jin
dc.contributor.editorMartínez-Minaya, Joaquín
dc.contributor.editorRodríguez-Álvarez, María Xosé
dc.date.accessioned2021-03-29T15:30:02Z
dc.date.available2021-03-29T15:30:02Z
dc.date.issued2020-07-20
dc.identifier273483400
dc.identifierae661164-e73c-4100-a051-621441a0c266
dc.identifier.citationAdam , T & Oelschläger , L 2020 , Hidden Markov models for multi-scale time series : an application to stock market data . in I Irigoien , D-J Lee , J Martínez-Minaya & M X Rodríguez-Álvarez (eds) , Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 - Bilbao, Basque Country, Spain . Universidad del País Vasco/Euskal Herriko Unibertsitatea , pp. 2-7 .en
dc.identifier.isbn9788413192673
dc.identifier.urihttps://hdl.handle.net/10023/21734
dc.description.abstractOver the last decades, hidden Markov models have emerged as a versatile class of statistical models for time series where the observed variables are driven by latent states. While conventional hidden Markov models are restricted to modeling single-scale data, economic variables are often observed at different temporal resolutions: an economy’s gross domestic product, for instance, is typically observed on a yearly, quarterly, or monthly basis, whereas stock prices are available daily or at even finer temporal resolutions. In this paper, we propose hierarchical hidden Markov models to incorporate such multi-scale data into a joint model, where we illustrate the suggested approach using 16 years of monthly trade volumes and daily log-returns of the Goldman Sachs stock.
dc.format.extent954061
dc.language.isoeng
dc.publisherUniversidad del País Vasco/Euskal Herriko Unibertsitatea
dc.relation.ispartofProceedings of the 35th International Workshop on Statistical Modellingen
dc.subjectHidden Markov modelsen
dc.subjectMulti-scale dataen
dc.subjectStock marketsen
dc.subjectTime series modelingen
dc.subjectTemporal resolutionen
dc.subjectHB Economic Theoryen
dc.subjectQA Mathematicsen
dc.subjectNSen
dc.subject.lccHBen
dc.subject.lccQAen
dc.titleHidden Markov models for multi-scale time series : an application to stock market dataen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.identifier.urlhttps://web-argitalpena.adm.ehu.es/listaproductos.asp?IdProducts=USPDF202673&titulo=Proceedings%20of%20the%2035th%20International%20Workshop%20on%20Statistical%20Modelling.%20July%2020-24,%202020%20-%20Bilbao,%20Basque%20Country,%20Spainen


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