Hidden Markov models for multi-scale time series: an application to stock market data

Timo Adam*, Lennart Oelschläger

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationProceedings of the 35th International Workshop on Statistical Modelling
Subtitle of host publicationJuly 20-24, 2020 - Bilbao, Basque Country, Spain
EditorsItziar Irigoien, Dae-Jin Lee, Joaquín Martínez-Minaya, María Xosé Rodríguez-Álvarez
PublisherUniversidad del País Vasco/Euskal Herriko Unibertsitatea
ISBN (Print)9788413192673
Publication statusPublished - 20 Jul 2020


  • Hidden Markov models
  • Multi-scale data
  • Stock markets
  • Time series modeling
  • Temporal resolution


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