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Long Memory in the Turkish Stock Market Return and Volatility

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  • Adnan Kasman
  • Erdost Torun

Abstract

This paper examines the dual long memory property of the Turkish stock market. The data set consists of daily returns, and long memory tests are carried out both for the returns and volatility. The results indicate that long memory dynamics in the returns and volatility might be modeled by using the ARFIMA-FIGARCH model. The results of the ARFIMAFIGARCH model show strong evidence of long memory in both returns and volatility. The long memory in returns implies that stock prices follow a predictable behavior, which is inconsistent with the efficient market hypothesis. The evidence of long memory in volatility, however, shows that uncertainty or risk is an important determinant of the behavior of daily stock data in the Turkish stock market.

Suggested Citation

  • Adnan Kasman & Erdost Torun, 2007. "Long Memory in the Turkish Stock Market Return and Volatility," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 7(2), pages 13-27.
    • Handle: RePEc:tcb:cebare:v:7:y:2007:i:2:p:13-27
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    1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    2. Assaf, A., 2006. "Dependence and mean reversion in stock prices: The case of the MENA region," Research in International Business and Finance, Elsevier, vol. 20(3), pages 286-304, September.
    3. Rehim Kili, 2004. "On the long memory properties of emerging capital markets: evidence from Istanbul stock exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 14(13), pages 915-922.
    4. Jussi Tolvi, 2003. "Long memory and outliers in stock market returns," Applied Financial Economics, Taylor & Francis Journals, vol. 13(7), pages 495-502.
    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. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    7. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute.
    8. Christos Floros & Shabbar Jaffry & Goncalo Valle Lima, 2007. "Long memory in the Portuguese stock market," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 24(3), pages 220-232, August.
    9. Barkoulas, John T. & Baum, Christopher F., 1996. "Long-term dependence in stock returns," Economics Letters, Elsevier, vol. 53(3), pages 253-259, December.
    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. Sadique, Shibley & Silvapulle, Param, 2001. "Long-Term Memory in Stock Market Returns: International Evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(1), pages 59-67, January.
    12. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    13. 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.
    14. Yin‐Wong Cheung, 1993. "Tests For Fractional Integration:A Monte Carlo Investigation," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(4), pages 331-345, July.
    15. 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.
    16. Cheung, Yin-Wong & Lai, Kon S., 1995. "A search for long memory in international stock market returns," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 597-615, August.
    17. Limam Imed, 2003. "Is Long Memory a Property of Thin Stock Markets? International Evidence Using Arab Countries," Review of Middle East Economics and Finance, De Gruyter, vol. 1(3), pages 56-71, December.
    18. Jacobsen, Ben, 1996. "Long term dependence in stock returns," Journal of Empirical Finance, Elsevier, vol. 3(4), pages 393-417, December.
    19. Olan Henry, 2002. "Long memory in stock returns: some international evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 12(10), pages 725-729.
    20. 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.
    21. Kang, Sang Hoon & Yoon, Seong-Min, 2007. "Long memory properties in return and volatility: Evidence from the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 591-600.
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    2. Hiremath, Gourishankar S & Bandi, Kamaiah, 2011. "Testing Long Memory in Stock Returns of Emerging Markets: Some Further Evidence," MPRA Paper 48517, University Library of Munich, Germany.
    3. Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023. "A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1801-1843, December.
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    5. Tripathy, Naliniprava, 2022. "Long memory and volatility persistence across BRICS stock markets," Research in International Business and Finance, Elsevier, vol. 63(C).
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    7. Pece Andreea Maria & Ludusan (Corovei) Emilia Anuta & Mutu Simona, 2013. "Testing The Long Range-Dependence For The Central Eastern European And The Balkans Stock Markets," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 1113-1124, July.
    8. Zhuhua Jiang & Walid Mensi & Seong-Min Yoon, 2023. "Risks in Major Cryptocurrency Markets: Modeling the Dual Long Memory Property and Structural Breaks," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    9. Muhammad Naeem & Hao Ji & Brunero Liseo, 2014. "Negative Return-Volume Relationship in Asian Stock Markets: Figarch-Copula Approach," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 2(2), pages 1-20.
    10. Mendoza Sandoval Sergio & Cruz Ake Salvador & Venegas Martínez Francisco, 2014. "Valuación con opciones reales de proyectos con flujos correlacionados con fundamentales económicos y con saltos extremos Viabilidad del caso COMERCI UCB," Contaduría y Administración, Accounting and Management, vol. 59(1), pages 63-93, enero-mar.
    11. Amira Akl Ahmed & Doaa Akl Ahmed, 2016. "Modelling Conditional Volatility and Downside Risk for Istanbul Stock Exchange," Working Papers 1028, Economic Research Forum, revised Jul 2016.
    12. Hiremath, Gourishankar S & Bandi, Kamaiah, 2010. "Long Memory in Stock Market Volatility:Evidence from India," MPRA Paper 48519, University Library of Munich, Germany.
    13. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2014. "Predicting BRICS stock returns using ARFIMA models," Applied Financial Economics, Taylor & Francis Journals, vol. 24(17), pages 1159-1166, September.

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    More about this item

    Keywords

    ARFIMA; FIGARCH; Long memory; Turkish stock market;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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