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

Yanlin Shi*, Kin-Yip Ho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)S189-S204
Number of pages16
JournalJournal of Banking and Finance
Issue numberSupp 2
Publication statusPublished - Dec 2015
Externally publishedYes


  • long memory
  • Markov regime-switching ARFIMA
  • regime switching


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