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Long memory in the volatility of an emerging equity market: The case of Turkey

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  • DiSario, Robert
  • Saraoglu, Hakan
  • McCarthy, Joseph
  • Li, Hsi
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    Abstract

    We use methods based on wavelets and aggregate series, which have gained growing acceptance in the finance literature, to test for long memory in the absolute value, squared, and log squared daily returns of the Istanbul Stock Exchange National 100 Index. Our results show that all three volatility series are characterized by long memory, indicating that shocks to the stock index volatility decay slowly and that distant observations of the series are associated with each other. There are several implications of our study for further research. First, models examining the volatility of the Turkish equity returns should include a long memory component in their parameter set. Second, tests should be conducted to assess whether such models result in an improvement in the volatility forecasts of the Turkish equity returns.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of International Financial Markets, Institutions and Money.

    Volume (Year): 18 (2008)
    Issue (Month): 4 (October)
    Pages: 305-312

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    Handle: RePEc:eee:intfin:v:18:y:2008:i:4:p:305-312

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    Web page: http://www.elsevier.com/locate/intfin

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    5. Lee, Jin, 2005. "Estimating memory parameter in the US inflation rate," Economics Letters, Elsevier, vol. 87(2), pages 207-210, May.
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    7. Marcus J. Chambers, . "Long Memory and Aggregation in Macroeconomic Time Series," Economics Discussion Papers 437, University of Essex, Department of Economics.
    8. Ramsey James B. & Lampart Camille, 1998. "The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(1), pages 1-22, April.
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    11. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    12. John T. Barkoulas & Christopher F. Baum & Nickolaos Travlos, 1996. "Long Memory in the Greek Stock Market," Boston College Working Papers in Economics 356., Boston College Department of Economics.
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    Cited by:
    1. Hiremath, Gourishankar S & Bandi, Kamaiah, 2010. "Long Memory in Stock Market Volatility:Evidence from India," MPRA Paper 48519, University Library of Munich, Germany.
    2. Kumar, Dilip, 2014. "Long range dependence in the high frequency USD/INR exchange rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 134-148.
    3. Chaker Aloui & Duc Khuong Nguyen, 2014. "On the detection of extreme movements and persistent behaviour in Mediterranean stock markets: a wavelet-based approach," Applied Economics, Taylor & Francis Journals, vol. 46(22), pages 2611-2622, August.
    4. Kaijian He & Kin Keung Lai & Guocheng Xiang, 2012. "Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach," Energies, MDPI, Open Access Journal, vol. 5(4), pages 1018-1043, April.
    5. Luis A. Gil-Alana & Yun Cao, 2010. "Stock market prices in China. Efficiency, mean reversion, long memory volatility and other implicit dynamics," Faculty Working Papers 12/11, School of Economics and Business Administration, University of Navarra.
    6. Lin, Xiaoqiang & Fei, Fangyu, 2013. "Long memory revisit in Chinese stock markets: Based on GARCH-class models and multiscale analysis," Economic Modelling, Elsevier, vol. 31(C), pages 265-275.

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