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Multifractal processes: Definition, properties and new examples

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  • Grahovac, Danijel

Abstract

We investigate stochastic processes possessing scale invariance properties which we refer to as multifractal processes. The examples of such processes known so far do not go much beyond the original cascade construction of Mandelbrot. We provide a new definition of the multifractal process by generalizing the definition of the self-similar process. We establish general properties of these processes and show how existing examples fit into our setting. Finally, we define a new class of examples inspired by the idea of Lamperti transformation. Namely, for any pair of infinitely divisible distribution and a stationary process one can construct a multifractal process.

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  • Grahovac, Danijel, 2020. "Multifractal processes: Definition, properties and new examples," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
  • Handle: RePEc:eee:chsofr:v:134:y:2020:i:c:s0960077920301375
    DOI: 10.1016/j.chaos.2020.109735
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    1. Barndorff-Nielsen, Ole E. & Pérez-Abreu, Victor, 1999. "Stationary and self-similar processes driven by Lévy processes," Stochastic Processes and their Applications, Elsevier, vol. 84(2), pages 357-369, December.
    2. Pavlov, A.N. & Semyachkina-Glushkovskaya, O.V. & Pavlova, O.N. & Abdurashitov, A.S. & Shihalov, G.M. & Rybalova, E.V. & Sindeev, S.S., 2016. "Multifractality in cerebrovascular dynamics: an approach for mechanisms-related analysis," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 210-213.
    3. Benoit Mandelbrot & Adlai Fisher & Laurent Calvet, 1997. "A Multifractal Model of Asset Returns," Cowles Foundation Discussion Papers 1164, Cowles Foundation for Research in Economics, Yale University.
    4. Kalamaras, N. & Philippopoulos, K. & Deligiorgi, D. & Tzanis, C.G. & Karvounis, G., 2017. "Multifractal scaling properties of daily air temperature time series," Chaos, Solitons & Fractals, Elsevier, vol. 98(C), pages 38-43.
    5. Laib, Mohamed & Golay, Jean & Telesca, Luciano & Kanevski, Mikhail, 2018. "Multifractal analysis of the time series of daily means of wind speed in complex regions," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 118-127.
    6. Emmanuel Bacry & Alexey Kozhemyak & J.-F. Muzy, 2008. "Continuous cascade models for asset returns," Post-Print hal-00604449, HAL.
    7. Bacry, E. & Kozhemyak, A. & Muzy, Jean-Francois, 2008. "Continuous cascade models for asset returns," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 156-199, January.
    8. Oleszkiewicz, Krzysztof, 2008. "On fake Brownian motions," Statistics & Probability Letters, Elsevier, vol. 78(11), pages 1251-1254, August.
    9. J. F. Muzy & R. Baile & E. Bacry, 2013. "Random cascade model in the limit of infinite integral scale as the exponential of a non-stationary $1/f$ noise. Application to volatility fluctuations in stock markets," Papers 1301.4160, arXiv.org.
    10. Pipiras,Vladas & Taqqu,Murad S., 2017. "Long-Range Dependence and Self-Similarity," Cambridge Books, Cambridge University Press, number 9781107039469, October.
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    1. Saâdaoui, Foued, 2024. "Segmented multifractal detrended fluctuation analysis for assessing inefficiency in North African stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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