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     Current Research Journal of Social Sciences


Long Memory Properties in Return and Volatility: An Application of the Impact of Arab Spring in Turkey Financial Market

1Pinar Cevik and 2Hamdi Emec
1Surveillance Department, Turkish Derivatives Exchange, Izmir, Turkey
2Departments of Econometrics, Dokuz Eylul University, Izmir, Turkey
Current Research Journal of Social Sciences  2013  2:60-66
http://dx.doi.org/10.19026/crjss.5.5540  |  © The Author(s) 2013
Received: January 05, 2013  |  Accepted: February 08, 2013  |  Published: March 25, 2013

Abstract

The Arab Spring which began on 17 December 2010 with the civil rebellions, revolutionary wave of demonstrations and protests in the Tunisia, Egypt, Libya, Yemen, Bahrain and Syria. The Arab Spring not only created a domino effect between Arabic countries but also it reflected a significant influence on the financial markets all over the world. The objective of this study is to analyze the impact of the Arab Spring in Turkey Financial Market in consideration of long memory. Long memory can be defined as the persistence of the unexpected shocks on the underlying has long lasting effects. Modeling long memory in stock returns and volatility has also attracted great deal of attention from finance literature recently. Existence of long memory is determined both for the returns and volatility of the time series by using different methods. Existence of long memory can be tested by Rescaled Range Statistics (R/S), Geweke and Porter-Hudak (GPH) Model and Gaussian Semi Parametric (GSP) Method. In consequence of these tests, if the stock returns have long memory affect then respectively Fractionally Integrated Autoregressive Moving Average Model (ARFIMA) and the Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH) model are used to detect the long memory in respectively return and volatility. In this study, the impact of the Arab Spring is investigated by modeled the long memory in Istanbul Stock Exchange using ISE 30 index prices in between December 17, 2010 and April 02, 2012.

Keywords:

ARFIMA, FIGARCH, GPH model, GSP method, long memory, R/S statistics,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2041-3246
ISSN (Print):   2041-3238
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