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Non stationary multifractality in stock returns

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  • Raffaello Morales
  • T. Di Matteo
  • Tomaso Aste

Abstract

We perform an extensive empirical analysis of scaling properties of equity returns, suggesting that financial data show time varying multifractal properties. This is obtained by comparing empirical observations of the weighted generalised Hurst exponent (wGHE) with time series simulated via Multifractal Random Walk (MRW) by Bacry \textit{et al.} [\textit{E.Bacry, J.Delour and J.Muzy, Phys.Rev.E \,{\bf 64} 026103, 2001}]. While dynamical wGHE computed on synthetic MRW series is consistent with a scenario where multifractality is constant over time, fluctuations in the dynamical wGHE observed in empirical data are not in agreement with a MRW with constant intermittency parameter. We test these hypotheses of constant multifractality considering different specifications of MRW model with fatter tails: in all cases considered, although the thickness of the tails accounts for most of anomalous fluctuations of multifractality, still cannot fully explain the observed fluctuations.

Suggested Citation

  • Raffaello Morales & T. Di Matteo & Tomaso Aste, 2012. "Non stationary multifractality in stock returns," Papers 1212.3195, arXiv.org, revised May 2013.
  • Handle: RePEc:arx:papers:1212.3195
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    1. 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.
    2. Buonocore, R.J. & Aste, T. & Di Matteo, T., 2016. "Measuring multiscaling in financial time-series," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 38-47.
    3. David, S.A. & Inácio, C.M.C. & Quintino, D.D. & Machado, J.A.T., 2020. "Measuring the Brazilian ethanol and gasoline market efficiency using DFA-Hurst and fractal dimension," Energy Economics, Elsevier, vol. 85(C).
    4. Douglas Castilho & Tharsis T. P. Souza & Soong Moon Kang & Jo~ao Gama & Andr'e C. P. L. F. de Carvalho, 2021. "Forecasting Financial Market Structure from Network Features using Machine Learning," Papers 2110.11751, arXiv.org.
    5. Sensoy, Ahmet & Tabak, Benjamin M., 2016. "Dynamic efficiency of stock markets and exchange rates," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 353-371.
    6. Lee, Hojin & Chang, Woojin, 2015. "Multifractal regime detecting method for financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 70(C), pages 117-129.
    7. Ausloos, Marcel & Jovanovic, Franck & Schinckus, Christophe, 2016. "On the “usual” misunderstandings between econophysics and finance: Some clarifications on modelling approaches and efficient market hypothesis," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 7-14.
    8. Sensoy, Ahmet & Hacihasanoglu, Erk, 2014. "Time-varying long range dependence in energy futures markets," Energy Economics, Elsevier, vol. 46(C), pages 318-327.
    9. Miguel Ángel Sánchez & Juan E Trinidad & José García & Manuel Fernández, 2015. "The Effect of the Underlying Distribution in Hurst Exponent Estimation," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-17, May.
    10. Kristjanpoller, Werner & Bouri, Elie & Takaishi, Tetsuya, 2020. "Cryptocurrencies and equity funds: Evidence from an asymmetric multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    11. Pagnottoni, Paolo & Spelta, Alessandro & Pecora, Nicolò & Flori, Andrea & Pammolli, Fabio, 2021. "Financial earthquakes: SARS-CoV-2 news shock propagation in stock and sovereign bond markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    12. Axel A. Araneda & Nils Bertschinger, 2020. "The sub-fractional CEV model," Papers 2001.06412, arXiv.org, revised Mar 2021.
    13. Araneda, Axel A. & Bertschinger, Nils, 2021. "The sub-fractional CEV model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    14. Leopoldo S'anchez-Cant'u & Carlos Arturo Soto-Campos & Andriy Kryvko, 2016. "Evidence of Self-Organization in Time Series of Capital Markets," Papers 1604.03996, arXiv.org, revised Mar 2017.
    15. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    16. Saâdaoui, Foued, 2023. "Skewed multifractal scaling of stock markets during the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    17. Lee, Hojin & Song, Jae Wook & Chang, Woojin, 2016. "Multifractal Value at Risk model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 113-122.
    18. Sensoy, Ahmet & Tabak, Benjamin M., 2015. "Time-varying long term memory in the European Union stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 147-158.
    19. Pagnottoni, Paolo & Spelta, Alessandro & Flori, Andrea & Pammolli, Fabio, 2022. "Climate change and financial stability: Natural disaster impacts on global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).

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