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Measuring capital market efficiency: Global and local correlations structure

  • Ladislav Kristoufek
  • Miloslav Vosvrda

We introduce a new measure for the capital market efficiency. The measure takes into consideration the correlation structure of the returns (long-term and short-term memory) and local herding behavior (fractal dimension). The efficiency measure is taken as a distance from an ideal efficient market situation. Methodology is applied to a portfolio of 41 stock indices. We find that the Japanese NIKKEI is the most efficient market. From geographical point of view, the more efficient markets are dominated by the European stock indices and the less efficient markets cover mainly Latin America, Asia and Oceania. The inefficiency is mainly driven by a local herding, i.e. a low fractal dimension.

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File URL: http://arxiv.org/pdf/1208.1298
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Paper provided by arXiv.org in its series Papers with number 1208.1298.

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Date of creation: Aug 2012
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Publication status: Published in Physica A 392(1), pp. 184-193, 2013
Handle: RePEc:arx:papers:1208.1298
Contact details of provider: Web page: http://arxiv.org/

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  1. Yan, Isabel K. & Chong, Terence & Lam, Tau-Hing, 2011. "Is the Chinese Stock Market Really Efficient," MPRA Paper 35219, University Library of Munich, Germany.
  2. Weron, Rafał, 2002. "Estimating long-range dependence: finite sample properties and confidence intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(1), pages 285-299.
  3. Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-617, December.
  4. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
  5. Ladislav Kristoufek, 2012. "How are rescaled range analyses affected by different memory and distributional properties? A Monte Carlo study," Papers 1201.3511, arXiv.org.
  6. Ladislav Kristoufek, 2012. "Fractal Markets Hypothesis and the Global Financial Crisis: Scaling, Investment Horizons and Liquidity," Papers 1203.4979, arXiv.org.
  7. Cajueiro, Daniel O. & Tabak, Benjamin M., 2007. "Long-range dependence and multifractality in the term structure of LIBOR interest rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 603-614.
  8. Podobnik, Boris & Fu, Dongfeng & Jagric, Timotej & Grosse, Ivo & Eugene Stanley, H., 2006. "Fractionally integrated process for transition economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 465-470.
  9. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
  10. Christopher F. Baum & John Barkoulas, 1996. "Long Term Dependence in Stock Returns," Boston College Working Papers in Economics 314., Boston College Department of Economics.
  11. Barunik, Jozef & Aste, Tomaso & Di Matteo, T. & Liu, Ruipeng, 2012. "Understanding the source of multifractality in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(17), pages 4234-4251.
  12. Eom, Cheoljun & Oh, Gabjin & Jung, Woo-Sung, 2008. "Relationship between efficiency and predictability in stock price change," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5511-5517.
  13. Epaminondas Panas, 2001. "Estimating fractal dimension using stable distributions and exploring long memory through ARFIMA models in Athens Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 11(4), pages 395-402.
  14. Di Matteo, T. & Aste, T. & Dacorogna, M.M., 2003. "Scaling behaviors in differently developed markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 183-188.
  15. Jozef Barunik & Ladislav Kristoufek, 2012. "On Hurst exponent estimation under heavy-tailed distributions," Papers 1201.4786, arXiv.org.
  16. Ladislav Krištoufek, 2010. "Rescaled Range Analysis and Detrended Fluctuation Analysis: Finite Sample Properties and Confidence Intervals," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 4(3), pages 315-329, November.
  17. Charles, Amélie & Darné, Olivier & Fouilloux, Jessica, 2011. "Testing the martingale difference hypothesis in CO2 emission allowances," Economic Modelling, Elsevier, vol. 28(1-2), pages 27-35, January.
  18. Lam, Keith S.K. & Tam, Lewis H.K., 2011. "Liquidity and asset pricing: Evidence from the Hong Kong stock market," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2217-2230, September.
  19. Carbone, A. & Castelli, G. & Stanley, H.E., 2004. "Time-dependent Hurst exponent in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 267-271.
  20. Barabási, Albert-László & Szépfalusy, Péter & Vicsek, Tamás, 1991. "Multifractal spectra of multi-affine functions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 178(1), pages 17-28.
  21. Cajueiro, Daniel O & Tabak, Benjamin M, 2004. "The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 521-537.
  22. Lim, Kian-Ping & Brooks, Robert D. & Kim, Jae H., 2008. "Financial crisis and stock market efficiency: Empirical evidence from Asian countries," International Review of Financial Analysis, Elsevier, vol. 17(3), pages 571-591, June.
  23. 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.
  24. Alvarez-Ramirez, Jose & Cisneros, Myriam & Ibarra-Valdez, Carlos & Soriano, Angel, 2002. "Multifractal Hurst analysis of crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 651-670.
  25. Alexeev, Vitali & Tapon, Francis, 2011. "Testing weak form efficiency on the Toronto Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 661-691, September.
  26. Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April.
  27. T. Di Matteo, 2007. "Multi-scaling in finance," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 21-36.
  28. Morales, Raffaello & Di Matteo, T. & Gramatica, Ruggero & Aste, Tomaso, 2012. "Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3180-3189.
  29. 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.
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