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.
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 language | English |
|---|---|
| Title of host publication | Proceedings of the 35th International Workshop on Statistical Modelling |
| Subtitle of host publication | July 20-24, 2020 - Bilbao, Basque Country, Spain |
| Editors | Itziar Irigoien, Dae-Jin Lee, Joaquín Martínez-Minaya, María Xosé Rodríguez-Álvarez |
| Publisher | Universidad del País Vasco/Euskal Herriko Unibertsitatea |
| Pages | 2-7 |
| ISBN (Print) | 9788413192673 |
| Publication status | Published - 20 Jul 2020 |
Keywords
- Hidden Markov models
- Multi-scale data
- Stock markets
- Time series modeling
- Temporal resolution