Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model

Recent research argues that if the cause of confusion between long memory and regime switching were properly controlled for, they could be effectively distinguished. Motivated by this idea, our study aims to distinguish between them of financial series. We firstly model long memory and regime switch...

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Published inJournal of banking & finance Vol. 61; pp. S189 - S204
Main Authors Shi, Yanlin, Ho, Kin-Yip
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.12.2015
Elsevier Sequoia S.A
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Abstract Recent research argues that if the cause of confusion between long memory and regime switching were properly controlled for, they could be effectively distinguished. Motivated by this idea, our study aims to distinguish between them of financial series. We firstly model long memory and regime switching via the Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Markov Regime-Switching (MRS) models, respectively. Their finite-sample properties and the confusion are investigated via simulations. To control for the cause of this confusion, we propose the MRS–ARFIMA model. A Monte Carlo study shows that this framework can effectively distinguish between the pure ARFIMA and pure MRS processes. Furthermore, MRS–ARFIMA outperforms the ordinary ARFIMA model for data simulated from the MRS–ARFIMA process. Finally, empirical studies of hourly and five-minute Garman–Klass and realized volatility of the FTSE index is conducted to demonstrate the advantages and usefulness of the proposed MRS–ARFIMA framework compared with the ARFIMA and MRS models in practice.
AbstractList Recent research argues that if the cause of confusion between long memory and regime switching were properly controlled for, they could be effectively distinguished. Motivated by this idea, our study aims to distinguish between them of financial series. We firstly model long memory and regime switching via the Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Markov Regime-Switching (MRS) models, respectively. Their finite-sample properties and the confusion are investigated via simulations. To control for the cause of this confusion, we propose the MRS-ARFIMA model. A Monte Carlo study shows that this framework can effectively distinguish between the pure ARFIMA and pure MRS processes. Furthermore, MRS-ARFIMA outperforms the ordinary ARFIMA model for data simulated from the MRS-ARFIMA process. Finally, empirical studies of hourly and five-minute Garman-Klass and realized volatility of the FTSE index is conducted to demonstrate the advantages and usefulness of the proposed MRS-ARFIMA framework compared with the ARFIMA and MRS models in practice. [web URL: http://www.sciencedirect.com/science/article/pii/S0378426615002368]
Recent research argues that if the cause of confusion between long memory and regime switching were properly controlled for, they could be effectively distinguished. Motivated by this idea, our study aims to distinguish between them of financial series. We firstly model long memory and regime switching via the Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Markov Regime-Switching (MRS) models, respectively. Their finite-sample properties and the confusion are investigated via simulations. To control for the cause of this confusion, we propose the MRS–ARFIMA model. A Monte Carlo study shows that this framework can effectively distinguish between the pure ARFIMA and pure MRS processes. Furthermore, MRS–ARFIMA outperforms the ordinary ARFIMA model for data simulated from the MRS–ARFIMA process. Finally, empirical studies of hourly and five-minute Garman–Klass and realized volatility of the FTSE index is conducted to demonstrate the advantages and usefulness of the proposed MRS–ARFIMA framework compared with the ARFIMA and MRS models in practice.
Author Shi, Yanlin
Ho, Kin-Yip
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  givenname: Kin-Yip
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C22
Regime switching
ARFIMA
RMSE
DGP
Long memory
SIRCA
MRS
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Markov Regime-Switching ARFIMA
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Snippet Recent research argues that if the cause of confusion between long memory and regime switching were properly controlled for, they could be effectively...
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StartPage S189
SubjectTerms ARFIMA
Effectiveness studies
Indexes
Long memory
Markov analysis
Markov Regime-Switching ARFIMA
Mathematical models
Monte Carlo simulation
Regime switching
Regression analysis
Volatility
Title Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model
URI https://dx.doi.org/10.1016/j.jbankfin.2015.08.025
https://www.proquest.com/docview/1754112256
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