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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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    2. 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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    5. Cornelis A. Los & Jeyanthi Karuppiah, 2004. "Wavelet Multiresolution Analysis of High-Frequency Asian FX Rates, Summer 1997," Finance, EconWPA 0409037, EconWPA.
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    8. Epaminondas Panas, 2001. "Estimating fractal dimension using stable distributions and exploring long memory through ARFIMA models in Athens Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 11(4), pages 395-402.
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    11. Mandelbrot, Benoit B, 1971. "When Can Price Be Arbitraged Efficiently? A Limit to the Validity of the Random Walk and Martingale Models," The Review of Economics and Statistics, MIT Press, vol. 53(3), pages 225-36, August.
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    13. Ray, Bonnie K & Tsay, Ruey S, 2000. "Long-Range Dependence in Daily Stock Volatilities," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 18(2), pages 254-62, April.
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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. 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.
    3. 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.
    4. 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.
    5. Chaker Aloui & Duc Khuong Nguyen, 2014. "On the detection of extreme movements and persistent behavior in Mediterranean stock markets: a wavelet-based approach," Working Papers 2014-184, Department of Research, Ipag Business School.
    6. Hiremath, Gourishankar S & Bandi, Kamaiah, 2010. "Long Memory in Stock Market Volatility:Evidence from India," MPRA Paper 48519, University Library of Munich, Germany.

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