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Nonlinearities in Stock Returns for Some Recent Entrants to the EU

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  • Barry Harrison
  • Winston Moore

Abstract

In this paper we use nonlinear tests to investigate the mean reverting properties of stock returns in a group of CEE markets. We also test whether returns in our target group of countries demonstrate characteristics of persistence and cross sectional dependence. Our results indicate that all series’ are stationary, but we find some ambiguity in the results of our tests for cross sectional dependence.

Suggested Citation

  • Barry Harrison & Winston Moore, 2010. "Nonlinearities in Stock Returns for Some Recent Entrants to the EU," NBS Discussion Papers in Economics 2010/1, Economics, Nottingham Business School, Nottingham Trent University.
    • Handle: RePEc:nbs:wpaper:2010/1
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    References listed on IDEAS

    as
    1. Mario Cerrato & Christian De Peretti & Nick Sarantis, 2007. "A nonlinear panel unit root test under cross section dependence," Documents de recherche 07-12, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
    2. Rockinger, Michael & Urga, Giovanni, 2001. "A Time-Varying Parameter Model to Test for Predictability and Integration in the Stock Markets of Transition Economies," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 73-84, January.
    3. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2003. "Testing for a unit root in the nonlinear STAR framework," Journal of Econometrics, Elsevier, vol. 112(2), pages 359-379, February.
    4. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    5. Barry Harrison & Winston Moore, 2009. "Spillover effects from London and Frankfurt to Central and Eastern European stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 19(18), pages 1509-1521.
    6. Bradley, Michael D. & Jansen, Dennis W., 2004. "Forecasting with a nonlinear dynamic model of stock returns and industrial production," International Journal of Forecasting, Elsevier, vol. 20(2), pages 321-342.
    7. Kim, Sei-Wan & Mollick, André V. & Nam, Kiseok, 2008. "Common nonlinearities in long-horizon stock returns: Evidence from the G-7 stock markets," Global Finance Journal, Elsevier, vol. 19(1), pages 19-31.
    8. repec:bla:obuest:v:61:y:1999:i:0:p:631-52 is not listed on IDEAS
    9. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    10. Choi, In, 2001. "Unit root tests for panel data," Journal of International Money and Finance, Elsevier, vol. 20(2), pages 249-272, April.
    11. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    12. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    13. Sarantis, Nicholas, 2001. "Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence," International Journal of Forecasting, Elsevier, vol. 17(3), pages 459-482.
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    Cited by:

    1. Barry Harrison & Winston Moore, 2012. "Stock Market Efficiency, Non-Linearity, Thin Trading and Asymmetric Information in MENA Stock Markets," Economic Issues Journal Articles, Economic Issues, vol. 17(1), pages 77-93, March.

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    More about this item

    Keywords

    nonlinearities; stock markets; Central and Eastern European Countries;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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