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Long Memory Behavior in the Returns of Pakistan Stock Market: ARFIMA-FIGARCH Models

Author

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  • Serpil TURKYILMAZ

    (Faculty of Arts & Sciences, Bilecik eyh Edebali University, Bilecik, Turkey.)

  • Mesut BALIBEY

    (Faculty of Arts & Sciences, Bilecik eyh Edebali University, Bilecik, Turkey.)

Abstract

This study examines the weak-form market efficiency of Pakistan Stock Market namely Karachi Stock Exchange for the period 2010-2013. The efficiency of stock market has tested by using ARFIMA-FIGARCH models estimated under different distribution assumptions as Normal, Student-t, Skewed Student- t and GED distribution. According to findings of study, ARFIMA model do not support long memory behaviour for the stock market returns. However, FIGARCH model indicate that volatility of market returns has long memory. Moreover, in order to test the feature of long memory in the return and volatility of the stock market simultaneously, ARFIMA-FIGARCH models are estimated according to different distributions simultaneously. Predictable structure of volatility of Pakistan Stock Market display that this market is the weak-form market inefficiency. Consequently, it is possible to say that technical analysis related to this stock market may be valid. This implies that it is possible to predict future stock prices and extra ordinary gains could be obtained trading in this market.

Suggested Citation

  • Serpil TURKYILMAZ & Mesut BALIBEY, 2014. "Long Memory Behavior in the Returns of Pakistan Stock Market: ARFIMA-FIGARCH Models," International Journal of Economics and Financial Issues, Econjournals, vol. 4(2), pages 400-410.
  • Handle: RePEc:eco:journ1:2014-02-16
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    References listed on IDEAS

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    1. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    2. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    3. Richard T. Baillie & Young-Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices," Working Papers 594, Queen Mary University of London, School of Economics and Finance.
    4. Jonathan Wright, 2002. "Log-Periodogram Estimation Of Long Memory Volatility Dependencies With Conditionally Heavy Tailed Returns," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 397-417.
    5. L.A. Gil‐Alana, 2006. "Fractional integration in daily stock market indexes," Review of Financial Economics, John Wiley & Sons, vol. 15(1), pages 28-48.
    6. Richard T. Baillie & Young-Wook Han & Robert J. Myers & Jeongseok Song, 2007. "Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices," Working Papers 594, Queen Mary University of London, School of Economics and Finance.
    7. Abdul Haque & Hung-Chun Liu & Fakhar-Un-Nisa, 2011. "Testing the Weak Form Efficiency of Pakistani Stock Market (2000 2010)," International Journal of Economics and Financial Issues, Econjournals, vol. 1(4), pages 153-162.
    8. Ercan Balaban, 1995. "Some Empirics of the Turkish Stock Market," Discussion Papers 9508, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    9. Marcelo Resende & Nilson Teixeira, 2002. "Permanent structural changes in the Brazilian economy and long memory: a stock market perspective," Applied Economics Letters, Taylor & Francis Journals, vol. 9(6), pages 373-375.
    10. Dimitrios Vougas, 2004. "Analysing long memory and volatility of returns in the Athens stock exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 14(6), pages 457-460.
    11. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    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.
    13. repec:ebl:ecbull:v:7:y:2003:i:3:p:1-13 is not listed on IDEAS
    14. Olan Henry, 2002. "Long memory in stock returns: some international evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 12(10), pages 725-729.
    15. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    16. Guglielmo Maria Caporale & Luis Gil-Alana, 2004. "Long range dependence in daily stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 14(6), pages 375-383.
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    Cited by:

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    More about this item

    Keywords

    Weak-Form Efficient Market Hypothesis; Long Memory; ARFIMA-FIGARCH model; Volatility.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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