Hidden Markov models for multi-scale time series: an application to stock market data
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Timo Adam [1] ; Lennart Oelschlger [2]
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[1] University of St Andrews
University of St Andrews
Reino Unido
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[2] Bielefeld University
Bielefeld University
Kreisfreie Stadt Bielefeld, Alemania
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- Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 2-7
- Idioma: inglés
- Enlaces
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Resumen
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.
