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Market efficiency and the Euro: the case of the Athens stock exchange

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  • Theodore Panagiotidis

Abstract

The efficient market hypothesis (EMH) is tested in the case of the Athens Stock Exchange (ASE) after the introduction of the euro. The underlying assumption is that stock prices would be more transparent; their performance easier to compare; the exchange rate risk eliminated and as a result we expect the new currency to strengthen argument in favour of the EMH. The General ASE Composite Index and the FTSE/ASE 20, which consists of “high capitalisation” companies, are used. Five statistical tests are employed to test the residuals of the random walk model: the BDS, McLeod-Li, Engle LM, Tsay and Bicovariance test. Bootstrap as well as asymptotic values of these tests are estimated. Alternative models from the GARCH family (GARCH, EGARCH and TGARCH) are also presented in order to investigate the behaviour of the series. Lastly, linear, asymmetric and non-linear error correction models are estimated and compared.
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  • Theodore Panagiotidis, 2010. "Market efficiency and the Euro: the case of the Athens stock exchange," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 37(3), pages 237-251, July.
    • Handle: RePEc:kap:empiri:v:37:y:2010:i:3:p:237-251
    DOI: 10.1007/s10663-008-9100-5
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    Cited by:

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    2. Theodore Panagiotidis, 2010. "An Out-of-Sample Test for Nonlinearity in Financial Time Series: An Empirical Application," Computational Economics, Springer;Society for Computational Economics, vol. 36(2), pages 121-132, August.
    3. Evzen Kocenda & Lubos Briatka, 2004. "Advancing the iid Test Based on Integration across the Correlation Integral: Ranges, Competition, and Power," CERGE-EI Working Papers wp235, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    4. George Filis, 2006. "Testing for Market Efficiency in Emerging Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 5(2), pages 121-133, August.
    5. Kian-Ping Lim & Weiwei Luo & Jae H. Kim, 2013. "Are US stock index returns predictable? Evidence from automatic autocorrelation-based tests," Applied Economics, Taylor & Francis Journals, vol. 45(8), pages 953-962, March.
    6. Mehmet Ali Balcı & Larissa M. Batrancea & Ömer Akgüller & Lucian Gaban & Mircea-Iosif Rus & Horia Tulai, 2022. "Fractality of Borsa Istanbul during the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(14), pages 1-33, July.
    7. Evzen Kocenda & Lubos Briatka, 2005. "Optimal Range for the iid Test Based on Integration Across the Correlation Integral," Econometric Reviews, Taylor & Francis Journals, vol. 24(3), pages 265-296.
    8. Mohammed AlHomaidy, 2020. "Lack of Reform Effect on Exchange Efficiency- Empirical Evidence from Saudi Market Index," Research in Applied Economics, Macrothink Institute, vol. 12(4), pages 46-65, December.
    9. David Chappell & Theodore Panagiotidis, 2005. "Using the correlation dimension to detect non-linear dynamics: Evidence from the Athens Stock Exchange," Econometrics 0504005, University Library of Munich, Germany.

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    More about this item

    Keywords

    Non-linearity; Market efficiency; Random walk; GARCH; Non-linear error correction; C22; C52; G10;
    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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