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Multifractality: Theory and Evidence an Application to the French Stock Market

Author

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  • Jérôme Fillol

    (MODEM - CNRS)

Abstract

This article presents the basics of multifractal modelling and shows the multifractal properties of the French Stock Market (CAC40). Monte Carlo simulations prove that the Multifractal Model of Asset Returns (MMAR) is a better model to replicate the scaling properties observed in the CAC40 series than alternative specifications like GARCH or FIGARCH.

Suggested Citation

  • Jérôme Fillol, 2003. "Multifractality: Theory and Evidence an Application to the French Stock Market," Economics Bulletin, AccessEcon, vol. 3(31), pages 1-12.
    • Handle: RePEc:ebl:ecbull:eb-03c50005
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Batten, Jonathan A. & Kinateder, Harald & Wagner, Niklas, 2014. "Multifractality and value-at-risk forecasting of exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 71-81.
    2. Samet Günay, 2016. "Performance of the Multifractal Model of Asset Returns (MMAR): Evidence from Emerging Stock Markets," IJFS, MDPI, vol. 4(2), pages 1-17, May.
    3. Julien Idier, 2011. "Long-term vs. short-term comovements in stock markets: the use of Markov-switching multifractal models," The European Journal of Finance, Taylor & Francis Journals, vol. 17(1), pages 27-48.
    4. Akash P. POOJARI & Siva Kiran GUPTHA & G Raghavender RAJU, 2022. "Multifractal analysis of equities. Evidence from the emerging and frontier banking sectors," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(3(632), A), pages 61-80, Autumn.
    5. Cajueiro, Daniel O. & Tabak, Benjamin M., 2006. "Testing for predictability in equity returns for European transition markets," Economic Systems, Elsevier, vol. 30(1), pages 56-78, March.
    6. Vogl, Markus, 2023. "Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framewo," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    7. Cajueiro, Daniel O. & Gogas, Periklis & Tabak, Benjamin M., 2009. "Does financial market liberalization increase the degree of market efficiency? The case of the Athens stock exchange," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 50-57, March.
    8. DIAF, Sami & TOUMACHE, Rachid, 2013. "Multifractal Analysis of the Algerian Dinar - US Dollar exchange rate," MPRA Paper 50701, University Library of Munich, Germany.
    9. Goddard, John & Onali, Enrico, 2016. "Long memory and multifractality: A joint test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 288-294.
    10. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW Kiel).
    11. Hallam, Mark & Olmo, Jose, 2014. "Forecasting daily return densities from intraday data: A multifractal approach," International Journal of Forecasting, Elsevier, vol. 30(4), pages 863-881.
    12. Sutthisit Jamdee & Cornelis A. Los, 2005. "Multifractal Modeling of the US Treasury Term Structure and Fed Funds Rate," Finance 0502021, University Library of Munich, Germany.

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    More about this item

    Keywords

    Multifractal model;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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