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Testing serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece

Author

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  • Paulo Ferreira

    (CEFAGE-UE (Center for Advanced Studies in Management and Economics of the University of Évora))

Abstract

This paper utilizes several tests to analyze serial dependence in financial data. In an attempt to provide a better explanation of the behavior of financial markets, we utilized tests that make use of mutual information and developed a detrended fluctuation analysis (DFA). Applying these tests to the series of stock market indexes of 10 European countries, we concluded for the absence of linear autocorrelation. However, with other tests, we found nonlinear serial dependence that affects the rates of return. Our results of mutual information and global correlation based tests confirmed such results. With DFA, we found out that most return rates series have long-range dependence, which appears to be more pronounced for Spain, Greece and Portugal. These conclusions could imply possibility of prediction in those series and thus the violation of the efficient market assumption.

Suggested Citation

  • Paulo Ferreira, 2012. "Testing serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece," CEFAGE-UE Working Papers 2012_24, University of Evora, CEFAGE-UE (Portugal).
    • Handle: RePEc:cfe:wpcefa:2012_24
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    References listed on IDEAS

    as
    1. Perron, Pierre & Vogelsang, Timothy J., "undated". "Level Shifts and Purchasing Power Parity," Instructional Stata datasets for econometrics levshift, Boston College Department of Economics.
    2. Muchnik, Lev & Bunde, Armin & Havlin, Shlomo, 2009. "Long term memory in extreme returns of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4145-4150.
    3. Menezes, Rui & Dionísio, Andreia & Hassani, Hossein, 2012. "On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 369-384.
    4. Marcelo Fernandes & Breno Neri, 2010. "Nonparametric Entropy-Based Tests of Independence Between Stochastic Processes," Econometric Reviews, Taylor & Francis Journals, vol. 29(3), pages 276-306.
    5. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: I. Empirical facts," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 991-1012.
    6. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo & Fernandez-Anaya, Guillermo, 2008. "Time-varying Hurst exponent for US stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6159-6169.
    7. A. I. McLeod & W. K. Li, 1983. "Diagnostic Checking Arma Time Series Models Using Squared‐Residual Autocorrelations," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 269-273, July.
    8. Yanhui Liu & Pierre Cizeau & Martin Meyer & Chung-Kang Peng & H. Eugene Stanley, 1997. "Correlations in Economic Time Series," Papers cond-mat/9706021, arXiv.org.
    9. Rui Menezes & Andreia Dionísio & Hossein Hassanic, 2010. "On the globalization of stock markets: An application of VECM, SSA technique and mutual information to the G7?," CEFAGE-UE Working Papers 2010_06, University of Evora, CEFAGE-UE (Portugal).
    10. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June.
    11. Darbellay, Georges A & Wuertz, Diethelm, 2000. "The entropy as a tool for analysing statistical dependences in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 429-439.
    12. Mariani, M.C. & Florescu, I. & Beccar Varela, M.P. & Ncheuguim, E., 2009. "Long correlations and Levy models applied to the study of memory effects in high frequency (tick) data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1659-1664.
    13. Enrico Scalas, 2006. "Five Years of Continuous-time Random Walks in Econophysics," Lecture Notes in Economics and Mathematical Systems, in: Akira Namatame & Taisei Kaizouji & Yuuji Aruka (ed.), The Complex Networks of Economic Interactions, pages 3-16, Springer.
    14. Clive Granger & Jin‐Lung Lin, 1994. "Using The Mutual Information Coefficient To Identify Lags In Nonlinear Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(4), pages 371-384, July.
    15. Perron, Pierre & Vogelsang, Timothy J, 1992. "Nonstationarity and Level Shifts with an Application to Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(3), pages 301-320, July.
    16. Ausloos, M., 2000. "Statistical physics in foreign exchange currency and stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 285(1), pages 48-65.
    17. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
    18. Jaroszewicz, Sebastian & Mariani, M. Cristina & Ferraro, Marta, 2005. "Long correlations and truncated Levy walks applied to the study Latin-American market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(2), pages 461-474.
    19. Perron, Pierre, 1997. "Further evidence on breaking trend functions in macroeconomic variables," Journal of Econometrics, Elsevier, vol. 80(2), pages 355-385, October.
    20. C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 649-669, September.
    21. Ausloos, M. & Vandewalle, N. & Boveroux, Ph. & Minguet, A. & Ivanova, K., 1999. "Applications of statistical physics to economic and financial topics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 274(1), pages 229-240.
    22. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    23. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: I. Empirical facts," Post-Print hal-00621058, HAL.
    24. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    25. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    26. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    Keywords

    Serial dependence; Stock indexes; Mutual information; Detrended fluctuation analysis; Nonlinearities.;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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