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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 | 273483400 | |
dc.identifier | ae661164-e73c-4100-a051-621441a0c266 | |
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.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.format.extent | 954061 | |
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.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.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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