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Fractal Market Hypothesis: The Emergent Financial Markets Case

Author

Listed:
  • Flavia BARNA

    (West University of Timisoara, Faculty of Economics and Business Administration)

  • Ştefana Maria DIMA

    (West University of Timisoara, Faculty of Economics and Business Administration)

  • Bogdan DIMA

    (West University of Timisoara, Faculty of Economics and Business Administration)

  • Lucian PAŞCA

    (West University of Timisoara, Faculty of Economics and Business Administration)

Abstract

The Efficient Market Hypothesis (EMH) is a long-stand frame in the analysis of the financial markets behaviour. Still, recent evidences point toward the limits of such as approach. Several alternative approaches have been proposed. Among them, Fractal Market Hypothesis (FMH) might provide interesting explanations for various types of market imperfections such as ‘fat tail’ effects, stochastic volatility and self-similarity. Based on this conceptual background, the aim of this study is twofold: 1) to directly address the issue of fractal dimension estimation by discussing some estimators which are more frequently used in literature and, respectively, 2) to provide empirical evidences for the potential fractal properties from nine important emergent markets. We find that emergent markets from Europe and Asia are closer to the ‘non-persistence’ status while Latin America markets exhibits more significant signs of local persistence. However, the current financial turmoil led to some changes in the time profile of the considered markets.

Suggested Citation

  • Flavia BARNA & Ştefana Maria DIMA & Bogdan DIMA & Lucian PAŞCA, 2016. "Fractal Market Hypothesis: The Emergent Financial Markets Case," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(2), pages 137-150.
    • Handle: RePEc:cys:ecocyb:v:50:y:2016:i:2:p:137-150
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    References listed on IDEAS

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    1. Hall, Joyce A. & Brorsen, B. Wade & Irwin, Scott H., 1989. "The Distribution of Futures Prices: A Test of the Stable Paretian and Mixture of Normals Hypotheses," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(1), pages 105-116, March.
    2. Ladislav Kristoufek & Miloslav Vosvrda, 2014. "Measuring capital market efficiency: long-term memory, fractal dimension and approximate entropy," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(7), pages 1-9, July.
    3. Hsing Fang & Kon S. Lai & Michael Lai, 1994. "Fractal structure in currency futures price dynamics," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 14(2), pages 169-181, April.
    4. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    5. Anderson, Nicola & Noss, Joseph, 2013. "Financial Stability Paper No 23: The Fractal Market Hypothesis and its implications for the stability of financial markets," Bank of England Financial Stability Papers 23, Bank of England.
    6. 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.
    7. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    8. Epaminondas Panas & Vassilia Ninni, 2010. "The Distribution of London Metal Exchange Prices: A Test of the Fractal Market Hypothesis," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 192-210.
    9. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
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    More about this item

    Keywords

    Financial markets; Fractal Market Hypothesis; fractal dimension; emergent markets.;
    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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