Abstract
Hidden Markov models are often applied in quantitative finance to capture the stylised facts of financial returns. They are usually discrete-time models and the number of states rarely exceeds two because of the quadratic increase in the number of parameters with the number of states. This paper presents an extension to continuous time where it is possible to increase the number of states with a linear rather than quadratic growth in the number of parameters. The possibility of increasing the number of states leads to a better fit to both the distributional and temporal properties of daily returns.
Original language | English |
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Pages (from-to) | 1531-1541 |
Journal | Quantitative Finance |
Volume | 15 |
Issue number | 9 |
DOIs | |
Publication status | Published - 2015 |
Bibliographical note
Published online 2015-02-10.Subject classification (UKÄ)
- Probability Theory and Statistics
Free keywords
- Hidden Markov models
- Continuous time
- Daily returns
- Leptokurtosis
- Volatility clustering
- Long memory
- C01—Econometrics
- C16—Specific distributions
- C22—Time-series models
- C52—Model evaluation and testing