Stylised facts of financial time series and hidden Markov models in continuous time
Peter, Nystrup ; Henrik, Madsen and Lindström, Erik LU
(2015)
In Quantitative Finance
15(9).
p.1531-1541
- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5049458
- author
- Peter, Nystrup
; Henrik, Madsen
and Lindström, Erik
LU
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- 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
- in
- Quantitative Finance
- volume
- 15
- issue
- 9
- pages
- 1531 - 1541
- publisher
- Taylor & Francis
- external identifiers
-
- wos:000359743900003
- scopus:84938741594
- ISSN
- 1469-7696
- DOI
- 10.1080/14697688.2015.1004801
- language
- English
- LU publication?
- yes
- additional info
- Published online 2015-02-10.
- id
- b739cc33-85e2-4dd8-810d-22bfa4ab4458 (old id 5049458)
- alternative location
- http://www.tandfonline.com/doi/full/10.1080/14697688.2015.1004801#abstract
- date added to LUP
- 2016-04-01 10:15:18
- date last changed
- 2022-04-12 03:35:26
@article{b739cc33-85e2-4dd8-810d-22bfa4ab4458,
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.}},
author = {{Peter, Nystrup and Henrik, Madsen and Lindström, Erik}},
issn = {{1469-7696}},
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}},
language = {{eng}},
number = {{9}},
pages = {{1531--1541}},
publisher = {{Taylor & Francis}},
series = {{Quantitative Finance}},
title = {{Stylised facts of financial time series and hidden Markov models in continuous time}},
url = {{http://dx.doi.org/10.1080/14697688.2015.1004801}},
doi = {{10.1080/14697688.2015.1004801}},
volume = {{15}},
year = {{2015}},
}