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 in | Journal of banking & finance Vol. 61; pp. S189 - S204 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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. |
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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 |
Author_xml | – sequence: 1 givenname: Yanlin surname: Shi fullname: Shi, Yanlin email: yanlin.shi@anu.edu.au organization: Research School of Finance, Actuarial Studies and Statistics, The Australian National University, Canberra, ACTON 2601, Australia – sequence: 2 givenname: Kin-Yip surname: Ho fullname: Ho, Kin-Yip organization: Research School of Finance, Actuarial Studies and Statistics, The Australian National University, Canberra, ACTON 2601, Australia |
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Keywords | FTSE C22 Regime switching ARFIMA RMSE DGP Long memory SIRCA MRS QMLE ACFs TRTH Markov Regime-Switching ARFIMA AIC BIC C51 |
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Title | Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model |
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