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Testing The Long Range-Dependence For The Central Eastern European And The Balkans Stock Markets

Author

Listed:
  • Pece Andreea Maria

    (Babes-Bolyai University, Babes-Bolyai University)

  • Ludusan (Corovei) Emilia Anuta

    (Babes-Bolyai University, Babes-Bolyai University)

  • Mutu Simona

    (Babes-Bolyai University, Babes-Bolyai University)

Abstract

In this study we tested the existence of long memory in the the return series for major Central Eastern European and Balkans stock markets, using the following statistical methods: Hurst Exponent, GPH method, Andrews and Guggenberger method, Reisen method, Willinger, Taqqu and Teverovsky method and ARFIMA model. The results obtained are mixed. The Hurst Exponent showed the existence of long memory in all indices, except PX. After applying the GPH method, the results showed that BET, ATHEX, SOFIX and CROBEX have a predictable behavior. The ARFIMA model results support the existence of long memory for BUX, SAX and BELEX. The predictable behavior of index returns may suggest that the CEE and Balkans stock markets are not weak form efficient.

Suggested Citation

  • 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.
    • Handle: RePEc:ora:journl:v:1:y:2013:i:1:p:1113-1124
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    References listed on IDEAS

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    1. Ibrahim A. Onour, 2010. "North Africa stock markets: analysis of long memory and persistence of shocks," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 3(2), pages 101-111.
    2. Mejra Festic & Alenka Kavkler & Silvo Dajcman, 2012. "Long memory in the Croatian and Hungarian stock market returns," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 30(1), pages 115-139.
    3. de Melo Mendes, Beatriz Vaz & Kolev, Nikolai, 2008. "How long memory in volatility affects true dependence structure," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 1070-1086, December.
    4. 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.
    5. 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.
    6. Silvo Dajcman, 2012. "Time-varying long-range dependence in stock market returns and financial market disruptions -- a case of eight European countries," Applied Economics Letters, Taylor & Francis Journals, vol. 19(10), pages 953-957, July.
    7. Ibrahim Onour, "undated". "North Africa Stock Markets: Analysis of Unit Root and Long Memory Process," API-Working Paper Series 0906, Arab Planning Institute - Kuwait, Information Center.
    8. Donald W. K. Andrews & Patrik Guggenberger, 2003. "A Bias--Reduced Log--Periodogram Regression Estimator for the Long--Memory Parameter," Econometrica, Econometric Society, vol. 71(2), pages 675-712, March.
    9. 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.
    10. Cajueiro, Daniel O. & Tabak, Benjamin M., 2005. "Testing for long range dependence in banking equity indices," Chaos, Solitons & Fractals, Elsevier, vol. 26(5), pages 1423-1428.
    11. Korkmaz, Turhan & Cevik, Emrah Ismail & Özataç, Nesrin, 2009. "Testing for long memory in ISE using Arfima-Figarch model and structural break test," MPRA Paper 71302, University Library of Munich, Germany.
    12. oh, Gabjin & Kim, Seunghwan & Eom, Cheoljun, 2008. "Long-term memory and volatility clustering in high-frequency price changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(5), pages 1247-1254.
    13. Cajueiro, Daniel O. & Tabak, Benjamin M., 2004. "Evidence of long range dependence in Asian equity markets: the role of liquidity and market restrictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 342(3), pages 656-664.
    14. Saadet Kasman & Evrim Turgutlu & A. Duygu Ayhan, 2009. "Long memory in stock returns: evidence from the major emerging Central European stock markets," Applied Economics Letters, Taylor & Francis Journals, vol. 16(17), pages 1763-1768.
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    Cited by:

    1. Tihana Škrinjarić & Branka Marasović & Boško Šego, 2021. "Does the Croatian Stock Market Have Seasonal Affective Disorder?," JRFM, MDPI, vol. 14(2), pages 1-16, February.
    2. Tihana Škrinjarić, 2018. "Testing for Seasonal Affective Disorder on Selected CEE and SEE Stock Markets," Risks, MDPI, vol. 6(4), pages 1-26, December.

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    More about this item

    Keywords

    emerging markets; long memory; market efficiency; ARFIMA model;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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