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Hidden Markov models for multi-scale time series : an application to stock market data
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dc.contributor.author | Adam, Timo | |
dc.contributor.author | Oelschläger, Lennart | |
dc.contributor.editor | Irigoien, Itziar | |
dc.contributor.editor | Lee, Dae-Jin | |
dc.contributor.editor | Martínez-Minaya, Joaquín | |
dc.contributor.editor | Rodríguez-Álvarez, María Xosé | |
dc.date.accessioned | 2021-03-29T15:30:02Z | |
dc.date.available | 2021-03-29T15:30:02Z | |
dc.date.issued | 2020-07-20 | |
dc.identifier.citation | Adam , 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.isbn | 9788413192673 | |
dc.identifier.other | PURE: 273483400 | |
dc.identifier.other | PURE UUID: ae661164-e73c-4100-a051-621441a0c266 | |
dc.identifier.uri | https://hdl.handle.net/10023/21734 | |
dc.description.abstract | Over 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.language.iso | eng | |
dc.publisher | Universidad del País Vasco/Euskal Herriko Unibertsitatea | |
dc.relation.ispartof | Proceedings of the 35th International Workshop on Statistical Modelling | en |
dc.rights | Copyright © 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 https://web-argitalpena.adm.ehu.es. | en |
dc.subject | Hidden Markov models | en |
dc.subject | Multi-scale data | en |
dc.subject | Stock markets | en |
dc.subject | Time series modeling | en |
dc.subject | Temporal resolution | en |
dc.subject | HB Economic Theory | en |
dc.subject | QA Mathematics | en |
dc.subject | NS | en |
dc.subject.lcc | HB | en |
dc.subject.lcc | QA | en |
dc.title | Hidden Markov models for multi-scale time series : an application to stock market data | en |
dc.type | Conference item | en |
dc.description.version | Publisher PDF | en |
dc.contributor.institution | University of St Andrews. Statistics | en |
dc.identifier.url | https://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,%20Spain | en |
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