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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

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.

Suggested Citation

  • DiSario, Robert & Saraoglu, Hakan & McCarthy, Joseph & Li, Hsi, 2008. "Long memory in the volatility of an emerging equity market: The case of Turkey," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(4), pages 305-312, October.
  • Handle: RePEc:eee:intfin:v:18:y:2008:i:4:p:305-312
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    Cited by:

    1. 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.
    2. Hiremath, Gourishankar S & Bandi, Kamaiah, 2010. "Long Memory in Stock Market Volatility:Evidence from India," MPRA Paper 48519, University Library of Munich, Germany.
    3. 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.
    4. 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.
    5. 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 1-26, April.
    6. Chaker Aloui, 2015. "Volatility forecasting and risk management in some MENA stock markets: a nonlinear framework," Afro-Asian Journal of Finance and Accounting, Inderscience Enterprises Ltd, vol. 5(2), pages 160-192.
    7. Luis A. Gil-Alana & Yun Cao, 2011. "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.
    8. Cevik, Emrah Ismail & Topaloğlu, Gültekin, 2014. "Volatilitede uzun hafıza ve yapısal kırılma: Borsa Istanbul örneği
      [Long memory and structural breaks on volatility: evidence from Borsa Istanbul]
      ," MPRA Paper 71485, University Library of Munich, Germany, revised 2014.
    9. Cevik, Emrah Ismail, 2012. "İstanbul Menkul Kıymetler Borsası’nda etkin piyasa hipotezinin uzun hafıza modelleri ile analizi: sektörel bazda bir inceleme
      [The testing of efficient market hypothesis in the Istanbul Stock Excha
      ," MPRA Paper 71484, University Library of Munich, Germany, revised 2012.
    10. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.

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