 Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA modelYanlin Shi and Kin-Yip Ho Journal of Banking & Finance, 2015, vol. 61, issue S2, S189-S204 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. Keywords: Long memory; Regime switching; ARFIMA; Markov Regime-Switching ARFIMA (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:61:y:2015:i:s2:p:s189-s204 DOI: 10.1016/j.jbankfin.2015.08.025 Access Statistics for this article Journal of Banking & Finance is currently edited by Ike Mathur More articles in Journal of Banking & Finance from Elsevier |
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