Long Memory Analysis: An Empirical Investigation

Authors

  • Rafik Nazarian Department of Economics, Islamic Azad University central Tehran Branch, Iran.
  • Esmaeil Naderi University of Tehran, Iran.
  • Nadiya Gandali Alikhani Science and Research Branch, Islamic Azad University, khouzestan-Iran.
  • Ashkan Amiri Department of Economics, Islamic Azad University central Tehran Branch, Iran.

Abstract

This study is an attempt to review the theory and applications of autoregressive fractionally integrated moving average (ARFIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models, mainly for the purpose of the description of the observed persistence in the mean and volatility of a time series. The long memory feature in FIGARCH models makes them a better candidate than other conditional heteroskedasticity models for modeling volatility in financial series. ARFIMA model also has a considerable capacity for modeling the return behavior of these time series. The daily data related to Tehran Stock Exchange (TSE) index was used for the purpose of this study. Considering the fact that the existence of conditional heteroskedasticity effects were confirmed in the stock return series, robust regression technique was used for estimation of different ARFIMA models. Furthermore, different GARCH-type models were also compared. The results of ARFIMA model are indicative of the absence of long memory in return series of the TSE index and the results from FIGARCH model show evidence of long memory in conditional variance of this series.

Keywords: stock market; long memory; ARFIMA; FIGARCH.

JEL Classifications: C13; C59; G10; G17

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Author Biographies

Rafik Nazarian, Department of Economics, Islamic Azad University central Tehran Branch, Iran.

Assistant Professor

Esmaeil Naderi, University of Tehran, Iran.

Faculty of Economics, University of TehranRanking: 301-400 in Shanghai Ranking of University

Nadiya Gandali Alikhani, Science and Research Branch, Islamic Azad University, khouzestan-Iran.

MA in Economics

Ashkan Amiri, Department of Economics, Islamic Azad University central Tehran Branch, Iran.

MA in Economics

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Published

2013-11-29

How to Cite

Nazarian, R., Naderi, E., Gandali Alikhani, N., & Amiri, A. (2013). Long Memory Analysis: An Empirical Investigation. International Journal of Economics and Financial Issues, 4(1), 16–26. Retrieved from https://www.econjournals.com/index.php/ijefi/article/view/606

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Articles