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Identifying the multifractional function of a Gaussian process

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  • Benassi, Albert
  • Cohen, Serge
  • Istas, Jacques

Abstract

Gaussian processes that are multifractional are studied in this paper. By multifractional processes we mean that they behave locally like a fractional Brownian motion, but the fractional index is no more a constant: it is a function. We introduce estimators of this multifractional function based on discrete observations of one sample path of the process and we study their asymptotical behavior as the mesh decreases to zero.

Suggested Citation

  • Benassi, Albert & Cohen, Serge & Istas, Jacques, 1998. "Identifying the multifractional function of a Gaussian process," Statistics & Probability Letters, Elsevier, vol. 39(4), pages 337-345, August.
    • Handle: RePEc:eee:stapro:v:39:y:1998:i:4:p:337-345
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    References listed on IDEAS

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    1. Andrey Feuerverger & Peter Hall & Andrew T. A. Wood, 1994. "Estimation Of Fractal Index And Fractal Dimension Of A Gaussian Process By Counting The Number Of Level Crossings," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(6), pages 587-606, November.
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    Cited by:

    1. Brouste, Alexandre & Istas, Jacques & Lambert-Lacroix, Sophie, 2007. "On Fractional Gaussian Random Fields Simulations," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i01).
    2. Garcin, Matthieu, 2017. "Estimation of time-dependent Hurst exponents with variational smoothing and application to forecasting foreign exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 462-479.
    3. Mohamed Boutahar & Gilles Dufrénot & Anne Péguin-Feissolle, 2008. "A Simple Fractionally Integrated Model with a Time-varying Long Memory Parameter d t," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 225-241, April.
    4. Istas, Jacques, 2007. "Quadratic variations of spherical fractional Brownian motions," Stochastic Processes and their Applications, Elsevier, vol. 117(4), pages 476-486, April.
    5. Ayoub Ammy-Driss & Matthieu Garcin, 2021. "Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics," Working Papers hal-02903655, HAL.
    6. Matthieu Garcin & Martino Grasselli, 2022. "Long versus short time scales: the rough dilemma and beyond," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 45(1), pages 257-278, June.
    7. Ayoub Ammy-Driss & Matthieu Garcin, 2020. "Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics," Papers 2007.10727, arXiv.org, revised Nov 2021.
    8. Matthieu Garcin & Martino Grasselli, 2020. "Long vs Short Time Scales: the Rough Dilemma and Beyond," Papers 2008.07822, arXiv.org, revised Nov 2021.
    9. Tsionas, Mike G., 2021. "Bayesian analysis of static and dynamic Hurst parameters under stochastic volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    10. Bégyn, Arnaud, 2007. "Functional limit theorems for generalized quadratic variations of Gaussian processes," Stochastic Processes and their Applications, Elsevier, vol. 117(12), pages 1848-1869, December.
    11. Ayache, Antoine & Lévy Véhel, Jacques, 2004. "On the identification of the pointwise Hölder exponent of the generalized multifractional Brownian motion," Stochastic Processes and their Applications, Elsevier, vol. 111(1), pages 119-156, May.
    12. Marco Dozzi & Yuliya Mishura & Georgiy Shevchenko, 2015. "Asymptotic behavior of mixed power variations and statistical estimation in mixed models," Statistical Inference for Stochastic Processes, Springer, vol. 18(2), pages 151-175, July.
    13. Frezza, Massimiliano & Bianchi, Sergio & Pianese, Augusto, 2021. "Fractal analysis of market (in)efficiency during the COVID-19," Finance Research Letters, Elsevier, vol. 38(C).
    14. Vu, Huong T.L. & Richard, Frédéric J.P., 2020. "Statistical tests of heterogeneity for anisotropic multifractional Brownian fields," Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 4667-4692.
    15. Pierre R. Bertrand & Abdelkader Hamdouni & Samia Khadhraoui, 2012. "Modelling NASDAQ Series by Sparse Multifractional Brownian Motion," Methodology and Computing in Applied Probability, Springer, vol. 14(1), pages 107-124, March.
    16. Matthieu Garcin, 2019. "Fractal analysis of the multifractality of foreign exchange rates [Analyse fractale de la multifractalité des taux de change]," Working Papers hal-02283915, HAL.
    17. Fabrice Gamboa & Jean-Michel Loubes, 2007. "Estimation of parameters of a multifractal process," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 383-407, August.
    18. Stoev, Stilian & Taqqu, Murad S. & Park, Cheolwoo & Michailidis, George & Marron, J.S., 2006. "LASS: a tool for the local analysis of self-similarity," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2447-2471, May.
    19. Bardet, Jean-Marc & Surgailis, Donatas, 2013. "Nonparametric estimation of the local Hurst function of multifractional Gaussian processes," Stochastic Processes and their Applications, Elsevier, vol. 123(3), pages 1004-1045.
    20. repec:jss:jstsof:23:i01 is not listed on IDEAS
    21. Ammy-Driss, Ayoub & Garcin, Matthieu, 2023. "Efficiency of the financial markets during the COVID-19 crisis: Time-varying parameters of fractional stable dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    22. Pierre R. Bertrand & Marie-Eliette Dury & Bing Xiao, 2020. "A study of Chinese market efficiency, Shanghai versus Shenzhen: Evidence based on multifractional models," Post-Print hal-03031766, HAL.
    23. Frezza, Massimiliano, 2012. "Modeling the time-changing dependence in stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 45(12), pages 1510-1520.
    24. Frezza, Massimiliano, 2014. "Goodness of fit assessment for a fractal model of stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 66(C), pages 41-50.
    25. Massimiliano Frezza & Sergio Bianchi & Augusto Pianese, 2022. "Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process," Computational Management Science, Springer, vol. 19(1), pages 99-132, January.

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