Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters
Nystrup, Peter ; Madsen, Henrik and Lindström, Erik LU
(2017)
In Journal of Forecasting
36(8).
p.989-1002
- Abstract
Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step density... (More)
Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations.
(Less)
- author
- Nystrup, Peter
; Madsen, Henrik
and Lindström, Erik
LU
- organization
- publishing date
- 2017-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Adaptive estimation, Daily returns, Hidden Markov models, Long memory, Time-varying parameters
- in
- Journal of Forecasting
- volume
- 36
- issue
- 8
- pages
- 989 - 1002
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:84987602233
- wos:000415900900007
- ISSN
- 0277-6693
- DOI
- 10.1002/for.2447
- language
- English
- LU publication?
- yes
- id
- 6168f868-bddf-48e3-ae40-2ee4cc4e4a01
- date added to LUP
- 2016-09-28 09:54:18
- date last changed
- 2024-06-14 14:35:12
@article{6168f868-bddf-48e3-ae40-2ee4cc4e4a01,
abstract = {{<p>Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations.</p>}},
author = {{Nystrup, Peter and Madsen, Henrik and Lindström, Erik}},
issn = {{0277-6693}},
keywords = {{Adaptive estimation; Daily returns; Hidden Markov models; Long memory; Time-varying parameters}},
language = {{eng}},
number = {{8}},
pages = {{989--1002}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Journal of Forecasting}},
title = {{Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters}},
url = {{http://dx.doi.org/10.1002/for.2447}},
doi = {{10.1002/for.2447}},
volume = {{36}},
year = {{2017}},
}