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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.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.otherPURE: 273483400
dc.identifier.otherPURE UUID: ae661164-e73c-4100-a051-621441a0c266
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.publisherUniversidad del País Vasco/Euskal Herriko Unibertsitatea
dc.relation.ispartofProceedings of the 35th International Workshop on Statistical Modellingen
dc.rightsCopyright © 2020 the Author(s). This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the final published version of the work, which was originally published at
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.titleHidden Markov models for multi-scale time series : an application to stock market dataen
dc.typeConference itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Statisticsen

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