IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v134y2020ics0960077920301375.html
   My bibliography  Save this article

Multifractal processes: Definition, properties and new examples

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920301375
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2020.109735?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    4. Pipiras,Vladas & Taqqu,Murad S., 2017. "Long-Range Dependence and Self-Similarity," Cambridge Books, Cambridge University Press, number 9781107039469, September.
    5. 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.
    6. 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.
    7. 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.
    8. Emmanuel Bacry & Alexey Kozhemyak & J.-F. Muzy, 2008. "Continuous cascade models for asset returns," Post-Print hal-00604449, HAL.
    9. 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.
    10. Oleszkiewicz, Krzysztof, 2008. "On fake Brownian motions," Statistics & Probability Letters, Elsevier, vol. 78(11), pages 1251-1254, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saâdaoui, Foued, 2024. "Segmented multifractal detrended fluctuation analysis for assessing inefficiency in North African stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Patrice Abry & Yannick Malevergne & Herwig Wendt & Marc Senneret & Laurent Jaffrès & Blaise Liaustrat, 2019. "Shuffling for understanding multifractality, application to asset price time series," Post-Print hal-02361738, HAL.
    2. Garnier, Josselin & Solna, Knut, 2019. "Emergence of turbulent epochs in oil prices," Chaos, Solitons & Fractals, Elsevier, vol. 122(C), pages 281-292.
    3. Josselin Garnier & Knut Solna, 2018. "Emergence of Turbulent Epochs in Oil Prices," Papers 1808.09382, arXiv.org, revised Apr 2019.
    4. M. Rypdal & O. L{o}vsletten, 2011. "Multifractal modeling of short-term interest rates," Papers 1111.5265, arXiv.org.
    5. Grahovac, Danijel & Leonenko, Nikolai N., 2014. "Detecting multifractal stochastic processes under heavy-tailed effects," Chaos, Solitons & Fractals, Elsevier, vol. 65(C), pages 78-89.
    6. Cristina Sattarhoff & Marc Gronwald, 2018. "How to Measure Financial Market Efficiency? A Multifractality-Based Quantitative Approach with an Application to the European Carbon Market," CESifo Working Paper Series 7102, CESifo.
    7. Caraiani, Petre & Haven, Emmanuel, 2015. "Evidence of multifractality from CEE exchange rates against Euro," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 395-407.
    8. 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).
    9. Kukacka, Jiri & Kristoufek, Ladislav, 2020. "Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    10. Lux, Thomas, 2008. "Applications of statistical physics in finance and economics," Kiel Working Papers 1425, Kiel Institute for the World Economy (IfW Kiel).
    11. 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.
    12. Thomas Lux, 2009. "Applications of Statistical Physics in Finance and Economics," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 9, Edward Elgar Publishing.
    13. Aslam, Faheem & Aziz, Saqib & Nguyen, Duc Khuong & Mughal, Khurrum S. & Khan, Maaz, 2020. "On the efficiency of foreign exchange markets in times of the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    14. Sattarhoff, Cristina & Lux, Thomas, 2021. "Forecasting the Variability of Stock Index Returns with the Multifractal Random Walk Model for Realized Volatilities," Economics Working Papers 2021-02, Christian-Albrechts-University of Kiel, Department of Economics.
    15. Garnier, Josselin & Solna, Knut, 2019. "Chaos and order in the bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 708-721.
    16. Grahovac, Danijel, 2022. "Intermittency in the small-time behavior of Lévy processes," Statistics & Probability Letters, Elsevier, vol. 187(C).
    17. Patrice Abry & Yannick Malevergne & Herwig Wendt & Stéphane Jaffard & Marc Senneret & Laurent Jaffrès, 2022. "Foreign Exchange Multivariate Multifractal Analysis," Post-Print hal-03735497, HAL.
    18. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Papers 1803.06917, arXiv.org.
    19. Rypdal, Martin & Løvsletten, Ola, 2013. "Modeling electricity spot prices using mean-reverting multifractal processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 194-207.
    20. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:134:y:2020:i:c:s0960077920301375. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.